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Sadeghi A, Sadeghi M, Fakhar M, Zakariaei Z, Sadeghi M, Bastani R. A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine. BMC Infect Dis 2024; 24:551. [PMID: 38824500 PMCID: PMC11144338 DOI: 10.1186/s12879-024-09428-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 05/23/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND Leishmaniasis, an illness caused by protozoa, accounts for a substantial number of human fatalities globally, thereby emerging as one of the most fatal parasitic diseases. The conventional methods employed for detecting the Leishmania parasite through microscopy are not only time-consuming but also susceptible to errors. Therefore, the main objective of this study is to develop a model based on deep learning, a subfield of artificial intelligence, that could facilitate automated diagnosis of leishmaniasis. METHODS In this research, we introduce LeishFuNet, a deep learning framework designed for detecting Leishmania parasites in microscopic images. To enhance the performance of our model through same-domain transfer learning, we initially train four distinct models: VGG19, ResNet50, MobileNetV2, and DenseNet 169 on a dataset related to another infectious disease, COVID-19. These trained models are then utilized as new pre-trained models and fine-tuned on a set of 292 self-collected high-resolution microscopic images, consisting of 138 positive cases and 154 negative cases. The final prediction is generated through the fusion of information analyzed by these pre-trained models. Grad-CAM, an explainable artificial intelligence technique, is implemented to demonstrate the model's interpretability. RESULTS The final results of utilizing our model for detecting amastigotes in microscopic images are as follows: accuracy of 98.95 1.4%, specificity of 98 2.67%, sensitivity of 100%, precision of 97.91 2.77%, F1-score of 98.92 1.43%, and Area Under Receiver Operating Characteristic Curve of 99 1.33. CONCLUSION The newly devised system is precise, swift, user-friendly, and economical, thus indicating the potential of deep learning as a substitute for the prevailing leishmanial diagnostic techniques.
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Affiliation(s)
- Alireza Sadeghi
- Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Mahdieh Sadeghi
- Student Research Committee, Mazandaran University of Medical Sciences, Sari, Iran
| | - Mahdi Fakhar
- Iranian National Registry Center for Lophomoniasis and Toxoplasmosis, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, P. O box, Sari, 48166-33131, Iran.
| | - Zakaria Zakariaei
- Toxicology and Forensic Medicine Division, Mazandaran Registry Center for Opioids Poisoning, Anti-microbial Resistance Research Center, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, Sari, Iran
| | | | - Reza Bastani
- Iranian National Registry Center for Lophomoniasis and Toxoplasmosis, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, P. O box, Sari, 48166-33131, Iran
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Yang Y, Lin M, Zhao H, Peng Y, Huang F, Lu Z. A survey of recent methods for addressing AI fairness and bias in biomedicine. J Biomed Inform 2024; 154:104646. [PMID: 38677633 PMCID: PMC11129918 DOI: 10.1016/j.jbi.2024.104646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/17/2024] [Indexed: 04/29/2024]
Abstract
OBJECTIVES Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns during model development, we surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV). Then we discussed the methods, such as data perturbation and adversarial learning, that have been applied in the biomedical domain to address bias. METHODS We performed our literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We then filtered the result of 10,041 articles automatically with loose constraints, and manually inspected the abstracts of the remaining 890 articles to identify the 55 articles included in this review. Additional articles in the references are also included in this review. We discuss each method and compare its strengths and weaknesses. Finally, we review other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness. RESULTS The bias of AIs in biomedicine can originate from multiple sources such as insufficient data, sampling bias and the use of health-irrelevant features or race-adjusted algorithms. Existing debiasing methods that focus on algorithms can be categorized into distributional or algorithmic. Distributional methods include data augmentation, data perturbation, data reweighting methods, and federated learning. Algorithmic approaches include unsupervised representation learning, adversarial learning, disentangled representation learning, loss-based methods and causality-based methods.
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Affiliation(s)
- Yifan Yang
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA; Department of Computer Science, University of Maryland, College Park, USA
| | - Mingquan Lin
- Department of Population Health Sciences, Weill Cornell Medicine, NY, USA
| | - Han Zhao
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, NY, USA
| | - Furong Huang
- Department of Computer Science, University of Maryland, College Park, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
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Vaikunta Pai T, Maithili K, Arun Kumar R, Nagaraju D, Anuradha D, Kumar S, Ravuri A, Sunilkumar Reddy T, Sivaram M, Vidhya RG. DKCNN: Improving deep kernel convolutional neural network-based covid-19 identification from CT images of the chest. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024:XST230424. [PMID: 38820059 DOI: 10.3233/xst-230424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
BACKGROUND An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease. OBJECTIVE A novel structure known as the Pointwise-Temporal-pointwise convolution unit is developed incorporated with the varying kernel-based depth wise temporal convolution before and after the pointwise convolution operations. METHODS The outcome is optimized by the Slap Swarm algorithm (SSA). The proposed Deep CNN is composed of depth wise temporal convolution and end-to-end automatic detection of disease. First, the datasets SARS-COV-2 Ct-Scan Dataset and CT scan COVID Prediction dataset are preprocessed using the min-max approach and the features are extracted for further processing. RESULTS The experimental analysis is conducted between the proposed and some state-of-art works and stated that the proposed work effectively classifies the disease than the other approaches. CONCLUSION The proposed structural unit is used to design the deep CNN with the increasing kernel sizes. The classification process is improved by the inclusion of depth wise temporal convolutions along with the kernel variation. The computational complexity is reduced by the introduction of stride convolutions are used in the residual linkage among the adjacent structural units.
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Affiliation(s)
- T Vaikunta Pai
- Department of Information Science and Engineering, NMAM Institute Of Technology-Affiliated To NITTE (Deemed To Be University), Bangalore, Karnataka, India
| | - K Maithili
- Department of Computer Science and Engineering (Ai & ML), KG Reddy College of Engineering and Technology, Hyderabad, Telangana, India
| | - Ravula Arun Kumar
- Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India
| | - D Nagaraju
- Department of Computer Science and Engineering, Sri Venkatesa Perumal College of Engineering and Technology, Puttur, Andhra Pradesh, India
| | - D Anuradha
- Department of Computer Science and Business Systems, Panimalar Engineering College, Chennai, India
| | - Shailendra Kumar
- Department of Electronics and Communication Engineering, Integral University Lucknow, Uttar Pradesh, India
| | | | - T Sunilkumar Reddy
- Department of Computer Science and Engineering, Sri Venkatesa Perumal College of Engineering and Technology, Puttur, Andhra Pradesh, India
| | - M Sivaram
- Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha Nagar, Thandalam, Tamil Nadu, India
| | - R G Vidhya
- Department of ECE, HKBKCE, Bangalore, India
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Cai Q, Zhang P, Xie F, Zhang Z, Tu B. Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle. BMC Med Imaging 2024; 24:102. [PMID: 38724896 PMCID: PMC11080198 DOI: 10.1186/s12880-024-01277-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
Precision and intelligence in evaluating the complexities of middle ear structures are required to diagnose auriculotemporal and ossicle-related diseases within otolaryngology. Due to the complexity of the anatomical details and the varied etiologies of illnesses such as trauma, chronic otitis media, and congenital anomalies, traditional diagnostic procedures may not yield accurate diagnoses. This research intends to enhance the diagnosis of diseases of the auriculotemporal region and ossicles by combining High-Resolution Spiral Computed Tomography (HRSCT) scanning with Deep Learning Techniques (DLT). This study employs a deep learning method, Convolutional Neural Network-UNet (CNN-UNet), to extract sub-pixel information from medical photos. This method equips doctors and researchers with cutting-edge resources, leading to groundbreaking discoveries and better patient healthcare. The research effort is the interaction between the CNN-UNet model and high-resolution Computed Tomography (CT) scans, automating activities including ossicle segmentation, fracture detection, and disruption cause classification, accelerating the diagnostic process and increasing clinical decision-making. The suggested HRSCT-DLT model represents the integration of high-resolution spiral CT scans with the CNN-UNet model, which has been fine-tuned to address the nuances of auriculotemporal and ossicular diseases. This novel combination improves diagnostic efficiency and our overall understanding of these intricate diseases. The results of this study highlight the promise of combining high-resolution CT scanning with the CNN-UNet model in otolaryngology, paving the way for more accurate diagnosis and more individualized treatment plans for patients experiencing auriculotemporal and ossicle-related disruptions.
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Affiliation(s)
- Qinfang Cai
- Department of Otolaryngology, The First Clinical Medical College of Jinan University, Guangzhou, 510630, Guangdong, China
- Department of Otolaryngology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, 510900, Guangdong, China
| | - Peishan Zhang
- Department of Otolaryngology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, 510900, Guangdong, China
| | - Fengmei Xie
- Department of Otolaryngology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, 510900, Guangdong, China
| | - Zedong Zhang
- Department of Otolaryngology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, 510900, Guangdong, China
| | - Bo Tu
- Department of Otolaryngology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, Guangdong, China.
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Zhao H, Deng X, Shao H, Jiang Y. COVID-19 diagnostic prediction on chest CT scan images using hybrid quantum-classical convolutional neural network. J Biomol Struct Dyn 2024; 42:3737-3746. [PMID: 38600864 DOI: 10.1080/07391102.2023.2226215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/11/2023] [Indexed: 04/12/2024]
Abstract
Notwithstanding the extensive research efforts directed towards devising a dependable approach for the diagnosis of coronavirus disease 2019 (COVID-19), the inherent complexity and capriciousness of the virus continue to pose a formidable challenge to the precise identification of affected individuals. In light of this predicament, it is essential to devise a model for COVID-19 prediction utilizing chest computed tomography (CT) scans. To this end, we present a hybrid quantum-classical convolutional neural network (HQCNN) model, which is founded on stochastic quantum circuits that can discern COVID-19 patients from chest CT images. Two publicly available chest CT image datasets were employed to evaluate the performance of our model. The experimental outcomes evinced diagnostic accuracies of 99.39% and 97.91%, along with precisions of 99.19% and 98.52%, respectively. These findings are indicative of the fact that the proposed model surpasses recently published works in terms of performance, thus providing a superior ability to precisely predict COVID-19 positive instances.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Haorong Zhao
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Xing Deng
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Haijian Shao
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV, USA
| | - Yingtao Jiang
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV, USA
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Rajinikanth V, Biju R, Mittal N, Mittal V, Askar S, Abouhawwash M. COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features. Heliyon 2024; 10:e27509. [PMID: 38468955 PMCID: PMC10926136 DOI: 10.1016/j.heliyon.2024.e27509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 02/29/2024] [Accepted: 02/29/2024] [Indexed: 03/13/2024] Open
Abstract
Several deep-learning assisted disease assessment schemes (DAS) have been proposed to enhance accurate detection of COVID-19, a critical medical emergency, through the analysis of clinical data. Lung imaging, particularly from CT scans, plays a pivotal role in identifying and assessing the severity of COVID-19 infections. Existing automated methods leveraging deep learning contribute significantly to reducing the diagnostic burden associated with this process. This research aims in developing a simple DAS for COVID-19 detection using the pre-trained lightweight deep learning methods (LDMs) applied to lung CT slices. The use of LDMs contributes to a less complex yet highly accurate detection system. The key stages of the developed DAS include image collection and initial processing using Shannon's thresholding, deep-feature mining supported by LDMs, feature optimization utilizing the Brownian Butterfly Algorithm (BBA), and binary classification through three-fold cross-validation. The performance evaluation of the proposed scheme involves assessing individual, fused, and ensemble features. The investigation reveals that the developed DAS achieves a detection accuracy of 93.80% with individual features, 96% accuracy with fused features, and an impressive 99.10% accuracy with ensemble features. These outcomes affirm the effectiveness of the proposed scheme in significantly enhancing COVID-19 detection accuracy in the chosen lung CT database.
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Affiliation(s)
- Venkatesan Rajinikanth
- Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India
| | - Roshima Biju
- Department of Computer Science Engineering, Parul University, Vadodara, 391760, Gujarat, India
| | - Nitin Mittal
- Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Palwal, 121102, Haryana, India
| | - Vikas Mittal
- Department of Electronics and Communication Engineering, Chandigarh University, Mohali, 140413, India
| | - S.S. Askar
- Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Mohamed Abouhawwash
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
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Ju H, Cui Y, Su Q, Juan L, Manavalan B. CODENET: A deep learning model for COVID-19 detection. Comput Biol Med 2024; 171:108229. [PMID: 38447500 DOI: 10.1016/j.compbiomed.2024.108229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/20/2024] [Accepted: 02/25/2024] [Indexed: 03/08/2024]
Abstract
Conventional COVID-19 testing methods have some flaws: they are expensive and time-consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some extent. However, there is no accurate and practical automatic diagnostic framework with good interpretability. The application of artificial intelligence (AI) technology to medical radiography can help to accurately detect the disease, reduce the burden on healthcare organizations, and provide good interpretability. Therefore, this study proposes a new deep neural network (CNN) based on CXR for COVID-19 diagnosis - CodeNet. This method uses contrastive learning to make full use of latent image data to enhance the model's ability to extract features and generalize across different data domains. On the evaluation dataset, the proposed method achieves an accuracy as high as 94.20%, outperforming several other existing methods used for comparison. Ablation studies validate the efficacy of the proposed method, while interpretability analysis shows that the method can effectively guide clinical professionals. This work demonstrates the superior detection performance of a CNN using contrastive learning techniques on CXR images, paving the way for computer vision and artificial intelligence technologies to leverage massive medical data for disease diagnosis.
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Affiliation(s)
- Hong Ju
- Heilongjiang Agricultural Engineering Vocational College, China
| | - Yanyan Cui
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Qiaosen Su
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
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Garg A, Alag S, Duncan D. CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes. Diagnostics (Basel) 2024; 14:337. [PMID: 38337853 PMCID: PMC10855975 DOI: 10.3390/diagnostics14030337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/06/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024] Open
Abstract
Given the pronounced impact COVID-19 continues to have on society-infecting 700 million reported individuals and causing 6.96 million deaths-many deep learning works have recently focused on the virus's diagnosis. However, assessing severity has remained an open and challenging problem due to a lack of large datasets, the large dimensionality of images for which to find weights, and the compute limitations of modern graphics processing units (GPUs). In this paper, a new, iterative application of transfer learning is demonstrated on the understudied field of 3D CT scans for COVID-19 severity analysis. This methodology allows for enhanced performance on the MosMed Dataset, which is a small and challenging dataset containing 1130 images of patients for five levels of COVID-19 severity (Zero, Mild, Moderate, Severe, and Critical). Specifically, given the large dimensionality of the input images, we create several custom shallow convolutional neural network (CNN) architectures and iteratively refine and optimize them, paying attention to learning rates, layer types, normalization types, filter sizes, dropout values, and more. After a preliminary architecture design, the models are systematically trained on a simplified version of the dataset-building models for two-class, then three-class, then four-class, and finally five-class classification. The simplified problem structure allows the model to start learning preliminary features, which can then be further modified for more difficult classification tasks. Our final model CoSev boosts classification accuracies from below 60% at first to 81.57% with the optimizations, reaching similar performance to the state-of-the-art on the dataset, with much simpler setup procedures. In addition to COVID-19 severity diagnosis, the explored methodology can be applied to general image-based disease detection. Overall, this work highlights innovative methodologies that advance current computer vision practices for high-dimension, low-sample data as well as the practicality of data-driven machine learning and the importance of feature design for training, which can then be implemented for improvements in clinical practices.
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Affiliation(s)
- Aksh Garg
- Computer Science Department, Stanford University, Stanford, CA 94305, USA; (A.G.); (S.A.)
| | - Shray Alag
- Computer Science Department, Stanford University, Stanford, CA 94305, USA; (A.G.); (S.A.)
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
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Abedi I, Vali M, Otroshi B, Zamanian M, Bolhasani H. HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation. BMC Res Notes 2024; 17:32. [PMID: 38254225 PMCID: PMC10804784 DOI: 10.1186/s13104-024-06693-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
INTRODUCTION Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the utilization of advanced methods. Due to privacy concerns, publicly available COVID-19 CT image datasets are incredibly tough to come by, leading to it being challenging to research and create AI-powered COVID-19 diagnostic algorithms based on CT images. DATA DESCRIPTION To address this issue, we created HRCTCov19, a new COVID-19 high-resolution chest CT scan image collection that includes not only COVID-19 cases of Ground Glass Opacity (GGO), Crazy Paving, and Air Space Consolidation but also CT images of cases with negative COVID-19. The HRCTCov19 dataset, which includes slice-level and patient-level labeling, has the potential to assist in COVID-19 research, in particular for diagnosis and a distinction using AI algorithms, machine learning, and deep learning methods. This dataset, which can be accessed through the web at http://databiox.com , includes 181,106 chest HRCT images from 395 patients labeled as GGO, Crazy Paving, Air Space Consolidation, and Negative.
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Affiliation(s)
- Iraj Abedi
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahsa Vali
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Bentolhoda Otroshi
- Department of Radiology, School of Medicine, Arak University of Medical Sciences, Arak, Iran
| | - Maryam Zamanian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hamidreza Bolhasani
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
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Rahman A, Debnath T, Kundu D, Khan MSI, Aishi AA, Sazzad S, Sayduzzaman M, Band SS. Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities. AIMS Public Health 2024; 11:58-109. [PMID: 38617415 PMCID: PMC11007421 DOI: 10.3934/publichealth.2024004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 12/18/2023] [Indexed: 04/16/2024] Open
Abstract
In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.
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Affiliation(s)
- Anichur Rahman
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Tanoy Debnath
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
- Department of CSE, Green University of Bangladesh, 220/D, Begum Rokeya Sarani, Dhaka -1207, Bangladesh
| | - Dipanjali Kundu
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Md. Saikat Islam Khan
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Airin Afroj Aishi
- Department of Computing and Information System, Daffodil International University, Savar, Dhaka, Bangladesh
| | - Sadia Sazzad
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Mohammad Sayduzzaman
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Shahab S. Band
- Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Taiwan
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Sadeghi A, Sadeghi M, Sharifpour A, Fakhar M, Zakariaei Z, Sadeghi M, Rokni M, Zakariaei A, Banimostafavi ES, Hajati F. Potential diagnostic application of a novel deep learning- based approach for COVID-19. Sci Rep 2024; 14:280. [PMID: 38167985 PMCID: PMC10762017 DOI: 10.1038/s41598-023-50742-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/24/2023] [Indexed: 01/05/2024] Open
Abstract
COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilities can be challenging, resulting in errors and further complications. Therefore, this study aims to develop deep learning models to facilitate automated diagnosis of COVID-19 from CT scan records of patients. The study also introduced COVID-MAH-CT, a new dataset that contains 4442 CT scan images from 133 COVID-19 patients, as well as 133 CT scan 3D volumes. We proposed and evaluated six different transfer learning models for slide-level analysis that are responsible for detecting COVID-19 in multi-slice spiral CT. Additionally, multi-head attention squeeze and excitation residual (MASERes) neural network, a novel 3D deep model was developed for patient-level analysis, which analyzes all the CT slides of a given patient as a whole and can accurately diagnose COVID-19. The codes and dataset developed in this study are available at https://github.com/alrzsdgh/COVID . The proposed transfer learning models for slide-level analysis were able to detect COVID-19 CT slides with an accuracy of more than 99%, while MASERes was able to detect COVID-19 patients from 3D CT volumes with an accuracy of 100%. These achievements demonstrate that the proposed models in this study can be useful for automatically detecting COVID-19 in both slide-level and patient-level from patients' CT scan records, and can be applied for real-world utilization, particularly in diagnosing COVID-19 cases in areas with limited medical facilities.
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Affiliation(s)
- Alireza Sadeghi
- Intelligent Mobile Robot Lab (IMRL), Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Mahdieh Sadeghi
- Student Research Committee, Mazandaran University of Medical Sciences, Sari, Iran
| | - Ali Sharifpour
- Pulmonary and Critical Care Division, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, Sari, Iran
| | - Mahdi Fakhar
- Iranian National Registry Center for Lophomoniasis and Toxoplasmosis, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, P.O Box: 48166-33131, Sari, Iran.
| | - Zakaria Zakariaei
- Toxicology and Forensic Medicine Division, Mazandaran Registry Center for Opioids Poisoning, Anti-microbial Resistance Research Center, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, P.O box: 48166-33131, Sari, Iran.
| | - Mohammadreza Sadeghi
- Student Research Committee, Mazandaran University of Medical Sciences, Sari, Iran
| | - Mojtaba Rokni
- Department of Radiology, Qaemshahr Razi Hospital, Mazandaran University of Medical Sciences, Sari, Iran
| | - Atousa Zakariaei
- MSC in Civil Engineering, European University of Lefke, Nicosia, Cyprus
| | - Elham Sadat Banimostafavi
- Department of Radiology, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, Sari, Iran
| | - Farshid Hajati
- Intelligent Technology Innovation Lab (ITIL) Group, Institute for Sustainable Industries and Liveable Cities, Victoria University, Footscray, Australia
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12
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Nur-A-Alam M, Nasir MK, Ahsan M, Based MA, Haider J, Kowalski M. Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN. Sci Rep 2023; 13:20063. [PMID: 37973820 PMCID: PMC10654719 DOI: 10.1038/s41598-023-47183-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023] Open
Abstract
The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.
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Affiliation(s)
- Md Nur-A-Alam
- Department of Computer Science & Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh
| | - Mostofa Kamal Nasir
- Department of Computer Science & Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, York, YO10 5GH, UK
| | - Md Abdul Based
- Department of Computer Science & Engineering, Dhaka International University, Dhaka, 1205, Bangladesh
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester St, Manchester, M1 5GD, UK
| | - Marcin Kowalski
- Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, Warsaw, Poland.
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13
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Ahmadinejad N, Ayyoubzadeh SM, Zeinalkhani F, Delazar S, Javanmard Z, Ahmadinejad Z, Mohajeri A, Esmaeili M. Discovering associations between radiological features and COVID-19 patients' deterioration. Health Sci Rep 2023; 6:e1257. [PMID: 37711676 PMCID: PMC10497911 DOI: 10.1002/hsr2.1257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/17/2023] [Accepted: 04/23/2023] [Indexed: 09/16/2023] Open
Abstract
Background and Aims Data mining methods are effective and well-known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID-19 by applying the rule mining method using characteristics of medical images. Methods This retrospective study has analyzed the radiological data from 104 COVID-19 hospitalized patients diagnosed with COVID-19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes. Results Ten rules were extracted with only X-ray-related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan-related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13. Conclusion This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID-19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.
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Affiliation(s)
- Nasrin Ahmadinejad
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
- Radiology Department, Cancer Institute, Imam Khomeini Hospital ComplexTehran University of Medical ScienceTehranIran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
| | - Fahimeh Zeinalkhani
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
- Radiology Department, Cancer Institute, Imam Khomeini Hospital ComplexTehran University of Medical ScienceTehranIran
| | - Sina Delazar
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
| | - Zohreh Javanmard
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
| | - Zahra Ahmadinejad
- Department of Infectious Diseases, Imam Khomeini Hospital ComplexTehran University of Medical SciencesTehranIran
| | | | - Marzieh Esmaeili
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
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14
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Liu Z, Lv Q, Yang Z, Li Y, Lee CH, Shen L. Recent progress in transformer-based medical image analysis. Comput Biol Med 2023; 164:107268. [PMID: 37494821 DOI: 10.1016/j.compbiomed.2023.107268] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/30/2023] [Accepted: 07/16/2023] [Indexed: 07/28/2023]
Abstract
The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.
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Affiliation(s)
- Zhaoshan Liu
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.
| | - Qiujie Lv
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore; School of Intelligent Systems Engineering, Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, 518107, China.
| | - Ziduo Yang
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore; School of Intelligent Systems Engineering, Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, 518107, China.
| | - Yifan Li
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.
| | - Chau Hung Lee
- Department of Radiology, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore.
| | - Lei Shen
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.
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15
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Santosh KC, GhoshRoy D, Nakarmi S. A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022. Healthcare (Basel) 2023; 11:2388. [PMID: 37685422 PMCID: PMC10486542 DOI: 10.3390/healthcare11172388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab, Vermillion, SD 57069, USA
| | - Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;
| | - Suprim Nakarmi
- Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA;
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16
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Application of a novel deep learning technique using CT images for COVID-19 diagnosis on embedded systems. ALEXANDRIA ENGINEERING JOURNAL 2023; 74:345-358. [PMCID: PMC10183629 DOI: 10.1016/j.aej.2023.05.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/24/2023] [Accepted: 05/08/2023] [Indexed: 11/04/2023]
Abstract
Problem A novel coronavirus (COVID-19) has created a worldwide pneumonia epidemic, and it's important to make a computer-aided way for doctors to use computed tomography (CT) images to find people with COVID-19 as soon as possible. Aim: A fully automated, novel deep-learning method for diagnosis and prognostic analysis of COVID-19 on the embedded system is presented. Methods In this study, CT scans are utilized to identify individuals with COVID-19, pneumonia, or normal class. To achieve classification two pre-trained CNN models, namely ResNet50 and MobileNetv2, which are commonly used for image classification tasks. Additionally, a novel CNN architecture called CovidxNet-CT is introduced specifically designed for COVID-19 diagnosis using three classes of CT scans. To evaluate the effectiveness of the proposed method, k-fold cross-validation is employed, which is a common approach to estimate the performance of deep learning. The study is also evaluated the proposed method on two embedded system platforms, Jetson Nano and Tx2, to demonstrate its feasibility for deployment in resource-constrained environments. Results With an average accuracy of %98.83 and an AUC of 0.988, the system is trained and verified using a 4 fold cross-validation approach. Conclusion The optimistic outcomes from the investigation propose that CovidxNet-CT has the capacity to support radiologists and contribute towards the efforts to combat COVID-19. This study proposes a fully automated, deep-learning-based method for COVID-19 diagnosis and prognostic analysis that is specifically designed for use on embedded systems.
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17
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Das S, Ayus I, Gupta D. A comprehensive review of COVID-19 detection with machine learning and deep learning techniques. HEALTH AND TECHNOLOGY 2023; 13:1-14. [PMID: 37363343 PMCID: PMC10244837 DOI: 10.1007/s12553-023-00757-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/14/2023] [Indexed: 06/28/2023]
Abstract
Purpose The first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement. Methods The researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected. Results In those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research. Conclusion In conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.
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Affiliation(s)
- Sreeparna Das
- Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh, Jote, Arunachal Pradesh 791113 India
| | - Ishan Ayus
- Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha 751030 India
| | - Deepak Gupta
- Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP 211004 India
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18
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Mozaffari J, Amirkhani A, Shokouhi SB. A survey on deep learning models for detection of COVID-19. Neural Comput Appl 2023; 35:1-29. [PMID: 37362568 PMCID: PMC10224665 DOI: 10.1007/s00521-023-08683-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-023-08683-x.
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Affiliation(s)
- Javad Mozaffari
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Abdollah Amirkhani
- School of Automotive Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Shahriar B. Shokouhi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
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19
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Córdova-Palomera A, Siffel C, DeBoever C, Wong E, Diogo D, Szalma S. Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19. PLoS One 2023; 18:e0285991. [PMID: 37235597 DOI: 10.1371/journal.pone.0285991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 05/05/2023] [Indexed: 05/28/2023] Open
Abstract
As findings on the epidemiological and genetic risk factors for coronavirus disease-19 (COVID-19) continue to accrue, their joint power and significance for prospective clinical applications remains virtually unexplored. Severity of symptoms in individuals affected by COVID-19 spans a broad spectrum, reflective of heterogeneous host susceptibilities across the population. Here, we assessed the utility of epidemiological risk factors to predict disease severity prospectively, and interrogated genetic information (polygenic scores) to evaluate whether they can provide further insights into symptom heterogeneity. A standard model was trained to predict severe COVID-19 based on principal component analysis and logistic regression based on information from eight known medical risk factors for COVID-19 measured before 2018. In UK Biobank participants of European ancestry, the model achieved a relatively high performance (area under the receiver operating characteristic curve ~90%). Polygenic scores for COVID-19 computed from summary statistics of the Covid19 Host Genetics Initiative displayed significant associations with COVID-19 in the UK Biobank (p-values as low as 3.96e-9, all with R2 under 1%), but were unable to robustly improve predictive performance of the non-genetic factors. However, error analysis of the non-genetic models suggested that affected individuals misclassified by the medical risk factors (predicted low risk but actual high risk) display a small but consistent increase in polygenic scores. Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. Associations between COVID-19 and genetic factors were statistically robust, but currently they have limited predictive power for translational settings. Despite that, the outcomes also suggest that severely affected cases with a medical history profile of low risk might be partly explained by polygenic factors, prompting development of boosted COVID-19 polygenic models based on new data and tools to aid risk-prediction.
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Affiliation(s)
- Aldo Córdova-Palomera
- Takeda Development Center Americas, Inc., San Diego, California, United States of America
| | - Csaba Siffel
- Takeda Development Center Americas, Inc., San Diego, California, United States of America
| | - Chris DeBoever
- Takeda Development Center Americas, Inc., San Diego, California, United States of America
| | - Emily Wong
- Takeda Development Center Americas, Inc., San Diego, California, United States of America
| | - Dorothée Diogo
- Takeda Development Center Americas, Inc., Cambridge, Massachusetts, United States of America
| | - Sandor Szalma
- Takeda Development Center Americas, Inc., San Diego, California, United States of America
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20
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Motta PC, Cortez PC, Silva BRS, Yang G, de Albuquerque VHC. Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans. Bioengineering (Basel) 2023; 10:529. [PMID: 37237599 PMCID: PMC10215490 DOI: 10.3390/bioengineering10050529] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the necessary level of care. This is especially important in the Intensive Care Unit to monitor disease progression or regression in the fight against this epidemic. To accomplish this, we merged public datasets from the literature to train lung and lesion segmentation models with five different distributions. We then trained eight CNN models for COVID-19 and Common-Acquired Pneumonia classification. If the examination was classified as COVID-19, we quantified the lesions and assessed the severity of the full CT scan. To validate the system, we used Resnetxt101 Unet++ and Mobilenet Unet for lung and lesion segmentation, respectively, achieving accuracy of 98.05%, F1-score of 98.70%, precision of 98.7%, recall of 98.7%, and specificity of 96.05%. This was accomplished in just 19.70 s per full CT scan, with external validation on the SPGC dataset. Finally, when classifying these detected lesions, we used Densenet201 and achieved accuracy of 90.47%, F1-score of 93.85%, precision of 88.42%, recall of 100.0%, and specificity of 65.07%. The results demonstrate that our pipeline can correctly detect and segment lesions due to COVID-19 and Common-Acquired Pneumonia in CT scans. It can differentiate these two classes from normal exams, indicating that our system is efficient and effective in identifying the disease and assessing the severity of the condition.
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Affiliation(s)
- Pedro Crosara Motta
- Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970, Brazil; (P.C.M.); (P.C.C.); (B.R.S.S.)
| | - Paulo César Cortez
- Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970, Brazil; (P.C.M.); (P.C.C.); (B.R.S.S.)
| | - Bruno R. S. Silva
- Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970, Brazil; (P.C.M.); (P.C.C.); (B.R.S.S.)
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK
| | - Victor Hugo C. de Albuquerque
- Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza 60455-970, Brazil; (P.C.M.); (P.C.C.); (B.R.S.S.)
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21
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Amiri M, Ranjbar M, Mohammadi GF. Automatic Diagnosis of COVID-19 Pneumonia using Artificial Intelligence Deep Learning Algorithm Based on Lung Computed Tomography Images. JOURNAL OF MEDICAL SIGNALS & SENSORS 2023; 13:110-117. [PMID: 37448542 PMCID: PMC10336915 DOI: 10.4103/jmss.jmss_146_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/20/2021] [Accepted: 01/02/2022] [Indexed: 07/15/2023]
Abstract
Background The lung computed tomography (CT) scan contains valuable information and patterns that provide the possibility of early diagnosis of COVID-19 disease as a global pandemic by the image processing software. In this research, based on deep learning of artificial intelligence, the software has been designed that is used clinically to diagnose COVID-19 disease with high accuracy. Methods Convolutional neural network architecture developed based on Inception-V3 for deep learning of lung image patterns, feature extraction, and image classification. The theory of transfer learning was utilized to increase the learning power of the system. Changes applied in the network layers to increase the detection power. The process of learning was repeated 30 times. All diagnostic statistical parameters of the diagnostic were analyzed to validate the software. Results Based on the data of Imam Khomeini Hospital in Sari, the validity, sensitivity, and accuracy of the software in diagnosing of affected to COVID-19 and nonaffected to it were obtained 98%, 98%, and 98%, respectively. Diagnostic statistical parameters on some data were 100%. The modified algorithm of Inception-V3 applied to heterogeneous data also had acceptable precision. Conclusion The proposed basic architecture of Inception-v3 utilized for this research has an admissible speed and exactness in learning CT scan images of patients' lungs, and diagnosis of COVID-19 pneumonia, which can be utilized clinically as a powerful diagnostic tool.
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Affiliation(s)
- Mohammad Amiri
- Assistant Professor, Department of Computer Engineering, Technical and Vocational University, Tehran, Iran
| | - Manizheh Ranjbar
- Lecturer, Department of Computer Engineering, Technical and Vocational University, Tehran, Iran
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22
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Wahid KA, Lin D, Sahin O, Cislo M, Nelms BE, He R, Naser MA, Duke S, Sherer MV, Christodouleas JP, Mohamed ASR, Murphy JD, Fuller CD, Gillespie EF. Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites. Sci Data 2023; 10:161. [PMID: 36949088 PMCID: PMC10033824 DOI: 10.1038/s41597-023-02062-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 03/10/2023] [Indexed: 03/24/2023] Open
Abstract
Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.
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Affiliation(s)
- Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Diana Lin
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Onur Sahin
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Michael Cislo
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mohammed A Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Simon Duke
- Department of Radiation Oncology, Cambridge University Hospitals, Cambridge, UK
| | - Michael V Sherer
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - John P Christodouleas
- Department of Radiation Oncology, The University of Pennsylvania Cancer Center, Philadelphia, PA, USA
- Elekta, Atlanta, GA, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - James D Murphy
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Erin F Gillespie
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Fred Hutchinson Cancer Center, Seattle, WA, USA.
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23
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Khademi S, Heidarian S, Afshar P, Enshaei N, Naderkhani F, Rafiee MJ, Oikonomou A, Shafiee A, Babaki Fard F, plataniotis KN, Mohammadi A. Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans. PLoS One 2023; 18:e0282121. [PMID: 36862633 PMCID: PMC9980818 DOI: 10.1371/journal.pone.0282121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/07/2023] [Indexed: 03/03/2023] Open
Abstract
The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the "SPGC-COVID" dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]), Normal sensitivity of 98.04% (95%CI: [89.55-99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI: [0.977-1]), 0.989 (95%CI: [0.962-1]), and 0.990 (95%CI: [0.971-1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach in improving the performance and robustness of the model when being evaluated on varied external test sets.
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Affiliation(s)
- Sadaf Khademi
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada
| | - Shahin Heidarian
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Parnian Afshar
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada
| | - Nastaran Enshaei
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada
| | - Farnoosh Naderkhani
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada
| | - Moezedin Javad Rafiee
- Department of Medicine and Diagnostic Radiology, McGill University, Montreal, QC, Canada
| | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Center, Toronto, Canada
| | - Akbar Shafiee
- Department of Cardiovascular Research, Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | | | | | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada
- * E-mail:
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24
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da Silveira TLT, Pinto PGL, Lermen TS, Jung CR. Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2023; 91:103775. [PMID: 36741546 PMCID: PMC9886432 DOI: 10.1016/j.jvcir.2023.103775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 01/18/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
The Coronavirus Disease 2019 (COVID-19) has drastically overwhelmed most countries in the last two years, and image-based approaches using computerized tomography (CT) have been used to identify pulmonary infections. Recent methods based on deep learning either require time-consuming per-slice annotations (2D) or are highly data- and hardware-demanding (3D). This work proposes a novel omnidirectional 2.5D representation of volumetric chest CTs that allows exploring efficient 2D deep learning architectures while requiring volume-level annotations only. Our learning approach uses a siamese feature extraction backbone applied to each lung. It combines these features into a classification head that explores a novel combination of Squeeze-and-Excite strategies with Class Activation Maps. We experimented with public and in-house datasets and compared our results with state-of-the-art techniques. Our analyses show that our method provides better or comparable prediction quality and accurately distinguishes COVID-19 infections from other kinds of pneumonia and healthy lungs.
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Affiliation(s)
- Thiago L T da Silveira
- Institute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Paulo G L Pinto
- Institute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Thiago S Lermen
- Institute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Cláudio R Jung
- Institute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
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25
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Hayat A, Baglat P, Mendonça F, Mostafa SS, Morgado-Dias F. Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1268. [PMID: 36674023 PMCID: PMC9858730 DOI: 10.3390/ijerph20021268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people's health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.
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Affiliation(s)
- Ahatsham Hayat
- University of Madeira, 9000-082 Funchal, Portugal
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | - Preety Baglat
- University of Madeira, 9000-082 Funchal, Portugal
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | - Fábio Mendonça
- University of Madeira, 9000-082 Funchal, Portugal
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | | | - Fernando Morgado-Dias
- University of Madeira, 9000-082 Funchal, Portugal
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
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26
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Mishra S, Dash TK, Panda G. Speech phoneme and spectral smearing based non-invasive COVID-19 detection. Front Artif Intell 2023; 5:1035805. [PMID: 36686850 PMCID: PMC9847386 DOI: 10.3389/frai.2022.1035805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/18/2022] [Indexed: 01/05/2023] Open
Abstract
COVID-19 is a deadly viral infection that mainly affects the nasopharyngeal and oropharyngeal cavities before the lung in the human body. Early detection followed by immediate treatment can potentially reduce lung invasion and decrease fatality. Recently, several COVID-19 detections methods have been proposed using cough and breath sounds. However, very little study has been done on the use of phoneme analysis and the smearing of the audio signal in COVID-19 detection. In this paper, this problem has been addressed and the classification of speech samples has been carried out in COVID-19-positive and healthy audio samples. Additionally, the grouping of the phonemes based on reference classification accuracies have been proposed for effectiveness and faster detection of the disease at a primary stage. The Mel and Gammatone Cepstral coefficients and their derivatives are used as the features for five standard machine learning-based classifiers. It is observed that the generalized additive model provides the highest accuracy of 97.22% for the phoneme grouping "/t//r//n//g//l/." This smearing-based phoneme classification technique can also be used in the future to classify other speech-related disease detections.
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Affiliation(s)
- Soumya Mishra
- Department of Electronics and Communication Engineering, C. V. Raman Global University, Bhubaneswar, India
| | - Tusar Kanti Dash
- Department of Electronics and Communication Engineering, C. V. Raman Global University, Bhubaneswar, India
| | - Ganapati Panda
- Department of Electronics and Communication Engineering, C. V. Raman Global University, Bhubaneswar, India
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27
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Hasan MM, Islam MU, Sadeq MJ, Fung WK, Uddin J. Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment. SENSORS (BASEL, SWITZERLAND) 2023; 23:527. [PMID: 36617124 PMCID: PMC9824505 DOI: 10.3390/s23010527] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.
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Affiliation(s)
- Md. Mahadi Hasan
- Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia 1349, Bangladesh
| | - Muhammad Usama Islam
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Muhammad Jafar Sadeq
- Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia 1349, Bangladesh
| | - Wai-Keung Fung
- Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
| | - Jasim Uddin
- Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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28
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Podder P, Das SR, Mondal MRH, Bharati S, Maliha A, Hasan MJ, Piltan F. LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases. SENSORS (BASEL, SWITZERLAND) 2023; 23:480. [PMID: 36617076 PMCID: PMC9824583 DOI: 10.3390/s23010480] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.
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Affiliation(s)
- Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Sanchita Rani Das
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - M. Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Azra Maliha
- Faculty of Engineering and IT, The British University in Dubai, Dubai P.O. Box 345015, United Arab Emirates
| | - Md Junayed Hasan
- National Subsea Centre, Robert Gordon University, Aberdeen AB10 7AQ, UK
| | - Farzin Piltan
- Ulsan Industrial Artificial Intelligence (UIAI) Lab, Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
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29
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Niranjan K, Shankar Kumar S, Vedanth S, Chitrakala DS. An Explainable AI driven Decision Support System for COVID-19 Diagnosis using Fused Classification and Segmentation. PROCEDIA COMPUTER SCIENCE 2023; 218:1915-1925. [PMID: 36743792 PMCID: PMC9886321 DOI: 10.1016/j.procs.2023.01.168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The coronavirus has caused havoc on billions of people worldwide. The Reverse Transcription Polymerase Chain Reaction(RT-PCR) test is widely accepted as a standard diagnostic tool for detecting infection, however, the severity of infection can't be measured accurately with RT-PCR results. Chest CT Scans of infected patients can manifest the presence of lesions with high sensitivity. During the pandemic, there is a dearth of competent doctors to examine chest CT images. Therefore, a Guided Gradcam based Explainable Classification and Segmentation system (GGECS) which is a real-time explainable classification and lesion identification decision support system is proposed in this work. The classification model used in the proposed GGECS system is inspired by Res2Net. Explainable AI techniques like GradCam and Guided GradCam are used to demystify Convolutional Neural Networks (CNNs). These explainable systems can assist in localizing the regions in the CT scan that contribute significantly to the system's prediction. The segmentation model can further reliably localize infected regions. The segmentation model is a fusion between the VGG-16 and the classification network. The proposed classification model in GGECS obtains an overall accuracy of 98.51 % and the segmentation model achieves an IoU score of 0.595.
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Affiliation(s)
- K Niranjan
- Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
| | - S Shankar Kumar
- Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
| | - S Vedanth
- Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
| | - Dr. S. Chitrakala
- Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
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30
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Bhatele KR, Jha A, Tiwari D, Bhatele M, Sharma S, Mithora MR, Singhal S. COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans. Cognit Comput 2022:1-38. [PMID: 36593991 PMCID: PMC9797382 DOI: 10.1007/s12559-022-10076-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 11/15/2022] [Indexed: 12/30/2022]
Abstract
This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.
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Affiliation(s)
| | - Anand Jha
- RJIT BSF Academy, Tekanpur, Gwalior India
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31
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Huang Y, Si Y, Hu B, Zhang Y, Wu S, Wu D, Wang Q. Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images. Comput Biol Med 2022; 150:106137. [PMID: 36191395 DOI: 10.1016/j.compbiomed.2022.106137] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/13/2022] [Accepted: 09/18/2022] [Indexed: 11/22/2022]
Abstract
In the past decade, deep learning methods have been implemented in the medical image fields and have achieved good performance. Recently, deep learning algorithms have been successful in the evaluation of diagnosis on lung images. Although chest radiography (CR) is the standard data modality for diagnosing pneumoconiosis, computed tomography (CT) typically provides more details of the lesions in the lung. Thus, a transformer-based factorized encoder (TBFE) was proposed and first applied for the classification of pneumoconiosis depicted on 3D CT images. Specifically, a factorized encoder consists of two transformer encoders. The first transformer encoder enables the interaction of intra-slice by encoding feature maps from the same slice of CT. The second transformer encoder explores the inter-slice interaction by encoding feature maps from different slices. In addition, the lack of grading standards on CT for labeling the pneumoconiosis lesions. Thus, an acknowledged CR-based grading system was applied to mark the corresponding pneumoconiosis CT stage. Then, we pre-trained the 3D convolutional autoencoder on the public LIDC-IDRI dataset and fixed the parameters of the last convolutional layer of the encoder to extract CT feature maps with underlying spatial structural information from our 3D CT dataset. Experimental results demonstrated the superiority of the TBFE over other 3D-CNN networks, achieving an accuracy of 97.06%, a recall of 89.33%, precision of 90%, and an F1-score of 93.33%, using 10-fold cross-validation.
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Affiliation(s)
- Yingying Huang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shanxi, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key laboratory of Biomedical Spectroscopy, Xi'an 710119, Shanxi, China.
| | - Yang Si
- Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, Department of Neurology, Chengdu, Sichuan, China; University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
| | - Bingliang Hu
- Key laboratory of Biomedical Spectroscopy, Xi'an 710119, Shanxi, China.
| | - Yan Zhang
- Department of Radiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Shuang Wu
- Department of Radiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Dongsheng Wu
- Department of Radiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Research Center of Artificial Intelligence in Medicine, West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, Sichuan, China.
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shanxi, China; Key laboratory of Biomedical Spectroscopy, Xi'an 710119, Shanxi, China.
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32
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Bhattacharjya U, Sarma KK, Medhi JP, Choudhury BK, Barman G. Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database. Biomed Signal Process Control 2022; 80:104297. [PMID: 36275840 PMCID: PMC9576693 DOI: 10.1016/j.bspc.2022.104297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 09/12/2022] [Accepted: 10/08/2022] [Indexed: 11/16/2022]
Abstract
Background and Objective : The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques. Methods : The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability. Results : Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement. Conclusion : Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.
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Affiliation(s)
- Upasana Bhattacharjya
- Department of Electronics and Communication Engineering Gauhati University, Guwahati 781014, Assam, India,Corresponding author
| | - Kandarpa Kumar Sarma
- Department of Electronics and Communication Engineering Gauhati University, Guwahati 781014, Assam, India
| | - Jyoti Prakash Medhi
- Department of Electronics and Communication Engineering Gauhati University, Guwahati 781014, Assam, India
| | - Binoy Kumar Choudhury
- Department of Radio Diagnosis and Imaging, Dr. Bhubaneswar Borooah Cancer Institute, Guwahati, Assam, India
| | - Geetanjali Barman
- Department of Radio Diagnosis and Imaging, Dr. Bhubaneswar Borooah Cancer Institute, Guwahati, Assam, India
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33
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Xu B, Martín D, Khishe M, Boostani R. COVID-19 diagnosis using chest CT scans and deep convolutional neural networks evolved by IP-based sine-cosine algorithm. Med Biol Eng Comput 2022; 60:2931-2949. [PMID: 35962266 PMCID: PMC9374292 DOI: 10.1007/s11517-022-02637-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 06/15/2022] [Indexed: 11/25/2022]
Abstract
The prevalence of the COVID-19 virus and its variants has influenced all aspects of our life, and therefore, the precise diagnosis of this disease is vital. If a polymerase chain reaction test for a subject is negative, but he/she cannot easily breathe, taking a computed tomography (CT) image from his/her lung is urgently recommended. This study aims to optimize a deep convolution neural network (DCNN) structure to increase the COVID-19 diagnosis accuracy in lung CT images. This paper employs the sine-cosine algorithm (SCA) to optimize the structure of DCNN to take raw CT images and determine their status. Three improvements based on regular SCA are proposed to enhance both the accuracy and speed of the results. First, a new encoding approach is proposed based on the internet protocol (IP) address. Then, an enfeebled layer is proposed to generate a variable-length DCNN. The suggested model is examined over the COVID-CT and SARS-CoV-2 datasets. The proposed method is compared to a standard DCNN and seven variable-length models in terms of five known metrics, including sensitivity, accuracy, specificity, F1-score, precision, and receiver operative curve (ROC) and precision-recall curves. The results demonstrate that the proposed DCNN-IPSCA surpasses other benchmarks, achieving final accuracy of (98.32% and 98.01%), the sensitivity of (97.22% and 96.23%), and specificity of (96.77% and 96.44%) on the SARS-CoV-2 and COVID-CT datasets, respectively. Also, the proposed DCNN-IPSCA performs much better than the standard DCNN, with GPU and CPU training times, which are 387.69 and 63.10 times faster, respectively.
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Affiliation(s)
- Binfeng Xu
- Guangdong Food and Drug Vocational College, Guangzhou, 510520, Guangdong, China.
| | - Diego Martín
- ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040, Madrid, Spain
| | - Mohammad Khishe
- Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran.
| | - Reza Boostani
- CSE & IT Department, Electrical and Computer Engineering Faculty, Shiraz University, Shiraz, Iran
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Anilkumar B, Srividya K, Mary Sowjanya A. Covid-19 classification using sigmoid based hyper-parameter modified DNN for CT scans and chest X-rays. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:12513-12536. [PMID: 36157352 PMCID: PMC9485800 DOI: 10.1007/s11042-022-13783-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 07/22/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Diagnosis of Computed Tomography (CT), and Chest X-rays (CXR) contains the problem of overfitting, earlier diagnosis, and mode collapse. In this work, we predict the classification of the Corona in CT and CXR images. Initially, the images of the dataset are pre-processed using the function of an adaptive Gaussian filter for de-nosing the image. Once the image is pre-processed it goes to Sigmoid Based Hyper-Parameter Modified DNN(SHMDNN). The hyperparameter modification makes use of the optimization algorithm of adaptive grey wolf optimization (AGWO). Finally, classification takes place and classifies the CT and CXR images into 3 categories namely normal, Pneumonia, and COVID-19 images. Better accuracy of 99.9% is reached when compared to different DNN networks.
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Affiliation(s)
- B Anilkumar
- Department of ECE, GMR Institute of Technology, Rajam, India
| | - K Srividya
- Department of CSE, GMR Institute of Technology, Rajam, India
| | - A Mary Sowjanya
- Department of CS&SE, Andhra University College of Engineering, Visakhapatnam, India
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35
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Zhao M, Li J, Xiang L, Zhang ZH, Peng SL. A diagnosis model of dementia via machine learning. Front Aging Neurosci 2022; 14:984894. [PMID: 36158565 PMCID: PMC9490175 DOI: 10.3389/fnagi.2022.984894] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
As the aging population poses serious challenges to families and societies, the issue of dementia has also received increasing attention. Dementia detection often requires a series of complex tests and lengthy questionnaires, which are time-consuming. In order to solve this problem, this article aims at the diagnosis method of questionnaire survey, hoping to establish a diagnosis model to help doctors make a diagnosis through machine learning method, and use feature selection method to select important questions to reduce the number of questions in the questionnaire, so as to reduce medical and time costs. In this article, Clinical Dementia Rating (CDR) is used as the data source, and various methods are used for modeling and feature selection, so as to combine similar attributes in the data set, reduce the categories, and finally use the confusion matrix to judge the effect. The experimental results show that the model established by the bagging method has the best effect, and the accuracy rate can reach 80% of the true diagnosis rate; in terms of feature selection, the principal component analysis (PCA) has the best effect compared with other methods.
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Affiliation(s)
- Ming Zhao
- School of Computer Science, Yangtze University, Jingzhou, China
| | - Jie Li
- School of Computer Science, Yangtze University, Jingzhou, China
| | - Liuqing Xiang
- School of Computer Science, Yangtze University, Jingzhou, China
| | - Zu-hai Zhang
- Department of Ophthalmology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
- *Correspondence: Zu-hai Zhang,
| | - Sheng-Lung Peng
- Department of Creative Technologies and Product Design, National Taipei University of Business, Taipei, Taiwan
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36
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Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl 2022; 34:15313-15348. [PMID: 35702664 PMCID: PMC9186489 DOI: 10.1007/s00521-022-07424-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/10/2022] [Indexed: 12/29/2022]
Abstract
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | | | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
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Heidari A, Toumaj S, Navimipour NJ, Unal M. A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain. Comput Biol Med 2022; 145:105461. [PMID: 35366470 PMCID: PMC8958272 DOI: 10.1016/j.compbiomed.2022.105461] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/13/2022] [Accepted: 03/24/2022] [Indexed: 12/16/2022]
Abstract
With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran,Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkey,Corresponding author
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
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38
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Liu B, Nie X, Li Z, Yang S, Tian Y. Evolving deep convolutional neural networks by IP-based marine predator algorithm for COVID-19 diagnosis using chest CT scans. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022:1-14. [PMID: 35646192 PMCID: PMC9127492 DOI: 10.1007/s12652-022-03901-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/04/2022] [Indexed: 05/27/2023]
Abstract
This paper proposes an optimal structured deep convolutional neural network (DCNN) based on the marine predator algorithm (MPA) to construct a novel automatic diagnosis platform that may help radiologists identify COVID-19 and non-COVID-19 patients based on CT scan categorization and analysis. The goal is met with the help of three modifications based on the regular MPA. First, a novel encoding scheme based on Internet Protocol (IP) addresses is proposed, followed by introducing an Enfeebled layer to build a variable-length DCNN. Finally, the learning process divides big datasets into smaller chunks that are randomly evaluated. The proposed model is compared to the COVID-CT and SARS-CoV-2 datasets to undertake a complete evaluation. Following that, the performance of the developed model (DCNN-IPMPA) is compared to that of a typical DCNN and seven variable-length models using five well-known comparison metrics, as well as the receiver operating characteristic and precision-recall curves. The results show that the DCNN-IPMPA outperforms other benchmarks, with a final accuracy of 97.21% on the SARS-CoV-2 dataset and 97.94% on the COVID-CT dataset. Also, timing analysis indicates that the DCNN processing time is the best among all benchmarks as expected; however, DCNN-IPMPA represents a competitive result compared to the standard DCNN.
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Affiliation(s)
- Bing Liu
- School of Software, Northwestern Polytechnical University, Xi’an, Shaanxi Province China
| | - Xuan Nie
- School of Software, Northwestern Polytechnical University, Xi’an, Shaanxi Province China
| | - Zhongxian Li
- School of Software, Northwestern Polytechnical University, Xi’an, Shaanxi Province China
| | - Shihong Yang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi Province China
| | - Yushu Tian
- Guiyang Fourth People’s Hospital, Guiyang City, Guizhou province China
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Ragab M, Alshehri S, Alhakamy NA, Mansour RF, Koundal D. Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6185013. [PMID: 35634055 PMCID: PMC9135545 DOI: 10.1155/2022/6185013] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/30/2022] [Accepted: 04/12/2022] [Indexed: 01/09/2023]
Abstract
It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model's training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.
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Affiliation(s)
- Mahmoud Ragab
- Department of Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Mathematics, Al-Azhar University, Nasercity 11884, Cairo, Egypt
| | - Samah Alshehri
- Department of Pharmacy Practice, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nabil A. Alhakamy
- Department of Pharmaceutics, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Excellence for Drug Research and Pharmaceutical Industries, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Mohamed Saeed Tamer Chair for Pharmaceutical Industries, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics, New Valley University, El-Kharga 72511, Egypt
| | - Deepika Koundal
- Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
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40
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Quintana-Ortí G, Chillarón M, Vidal V, Verdú G. High-performance reconstruction of CT medical images by using out-of-core methods in GPU. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106725. [PMID: 35290900 DOI: 10.1016/j.cmpb.2022.106725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/18/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Since Computed Tomography (CT) is one of the most widely used medical imaging tests, it is essential to work on methods that reduce the radiation the patient is exposed to. Although there are several possible approaches to achieve this, we focus on reducing the exposure time through sparse sampling. With this approach, efficient algebraic methods are needed to be able to generate the images in real time, and since their computational cost is high, using high-performance computing is essential. METHODS In this paper we present a GPU (Graphics Processing Unit) software for solving the CT image reconstruction problem using the QR factorization performed with out-of-core (OOC) techniques. This implementation is optimized to reduce the data transfer times between disk, CPU, and GPU, as well as to overlap input/output operations and computations. RESULTS The experimental study shows that a block cache stored on main page-locked memory is more efficient than using a cache on GPU memory or mirroring it in both GPU and CPU memory. Compared to a CPU version, this implementation is up to 6.5 times faster, providing an improved image quality when compared to other reconstruction methods. CONCLUSIONS The software developed is an optimized version of the QR factorization for GPU that allows the algebraic reconstruction of CT images with high quality and resolution, with a performance that can be compared with state-of-the-art methods used in clinical practice. This approach allows reducing the exposure time of the patient and thus the radiation dose.
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Affiliation(s)
- Gregorio Quintana-Ortí
- Depto. de Ingeniería y Ciencia de Computadores, Universitat Jaume I, Castellón, 12.071, Spain.
| | - Mónica Chillarón
- Instituto de Seguridad Industrial, Radiofísica y Medioambiental, Universitat Politècnica de València, Valencia, 46.022, Spain.
| | - Vicente Vidal
- Depto. de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, 46.022, Spain.
| | - Gumersindo Verdú
- Instituto de Seguridad Industrial, Radiofísica y Medioambiental, Universitat Politècnica de València, Valencia, 46.022, Spain.
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41
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Yurasakpong L, Asuvapongpatana S, Weerachatyanukul W, Meemon K, Jongkamonwiwat N, Kruepunga N, Chaiyamoon A, Sudsang T, Iwanaga J, Tubbs RS, Suwannakhan A. Anatomical variants identified on chest computed tomography of 1000+ COVID-19 patients from an open-access dataset. Clin Anat 2022; 35:723-731. [PMID: 35385153 PMCID: PMC9083245 DOI: 10.1002/ca.23873] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/01/2022] [Accepted: 04/02/2022] [Indexed: 12/03/2022]
Abstract
Chest computed tomography (CT) has been the preferred imaging modality during the pandemic owing to its sensitivity in detecting COVID‐19 infections. Recently, a large number of COVID‐19 imaging datasets have been deposited in public databases, leading to rapid advances in COVID‐19 research. However, the application of these datasets beyond COVID‐19‐related research has been little explored. The authors believe that they could be used in anatomical research to elucidate the link between anatomy and disease and to study disease‐related alterations to normal anatomy. Therefore, the present study was designed to investigate the prevalence of six well‐known anatomical variants in the thorax using open‐access CT images obtained from over 1000 Iranian COVID‐19 patients aged between 6 and 89 years (60.9% male and 39.1% female). In brief, we found that the azygos lobe, tracheal bronchus, and cardiac bronchus were present in 0.8%, 0.2%, and 0% of the patients, respectively. Variations of the sternum, including sternal foramen, episternal ossicles, and sternalis muscle, were observed in 9.6%, 2.9%, and 1.5%, respectively. We believe anatomists could benefit from using open‐access datasets as raw materials for research because these datasets are freely accessible and are abundant, though further research is needed to evaluate the uses of other datasets from different body regions and imaging modalities. Radiologists should also be aware of these common anatomical variants when examining lung CTs, especially since the use of this imaging modality has increased during the pandemic.
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Affiliation(s)
- Laphatrada Yurasakpong
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand.,In Silico and Clinical Anatomy Research Group (iSCAN), Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand
| | | | | | - Krai Meemon
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand
| | | | - Nutmethee Kruepunga
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand.,In Silico and Clinical Anatomy Research Group (iSCAN), Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Arada Chaiyamoon
- Department of Anatomy, Faculty of Medicine, Khon Kaen University, KhonKaen, Thailand
| | - Thanwa Sudsang
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Joe Iwanaga
- Department of Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana.,Department of Neurology, Tulane University School of Medicine, New Orleans, Louisiana.,Dental and Oral Medical Center, Kurume University School of Medicine, Fukuoka, Japan.,Department of Anatomy, Kurume University School of Medicine, Fukuoka, Japan
| | - R Shane Tubbs
- Department of Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana.,Department of Structural and Cellular Biology, Tulane University School of Medicine, New Orleans, Louisiana.,Department of Neurosurgery and Ochsner Neuroscience Institute, Ochsner Health System, New Orleans, Louisiana.,Department of Anatomical Sciences, St. George's University St. George.'s, Grenada
| | - Athikhun Suwannakhan
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand.,In Silico and Clinical Anatomy Research Group (iSCAN), Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand
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42
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Aruleba RT, Adekiya TA, Ayawei N, Obaido G, Aruleba K, Mienye ID, Aruleba I, Ogbuokiri B. COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods. Bioengineering (Basel) 2022; 9:153. [PMID: 35447713 PMCID: PMC9024895 DOI: 10.3390/bioengineering9040153] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 12/15/2022] Open
Abstract
As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.
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Affiliation(s)
- Raphael Taiwo Aruleba
- Department of Molecular and Cell Biology, Faculty of Science, University of Cape Town, Cape Town 7701, South Africa;
| | - Tayo Alex Adekiya
- Department of Pharmacy and Pharmacology, School of Therapeutic Science, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa;
| | - Nimibofa Ayawei
- Department of Chemistry, Bayelsa Medical University, Yenagoa PMB 178, Bayelsa State, Nigeria;
| | - George Obaido
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA
| | - Kehinde Aruleba
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Ibomoiye Domor Mienye
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Idowu Aruleba
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Blessing Ogbuokiri
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
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43
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Abdulkareem KH, Mostafa SA, Al-Qudsy ZN, Mohammed MA, Al-Waisy AS, Kadry S, Lee J, Nam Y. Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5329014. [PMID: 35368962 PMCID: PMC8968354 DOI: 10.1155/2022/5329014] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/29/2022] [Accepted: 02/18/2022] [Indexed: 12/24/2022]
Abstract
Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.
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Affiliation(s)
| | - Salama A. Mostafa
- Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia
| | - Zainab N. Al-Qudsy
- Computer Sciences Department, Baghdad College of Economic Sciences University, Baghdad, Iraq
| | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, 11 Ramadi, Anbar, Iraq
| | - Alaa S. Al-Waisy
- Communications Engineering Techniques Department Information Technology Collage, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
| | - Seifedine Kadry
- Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway
| | - Jinseok Lee
- Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Republic of Korea
| | - Yunyoung Nam
- Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
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Vaidyanathan A, Guiot J, Zerka F, Belmans F, Van Peufflik I, Deprez L, Danthine D, Canivet G, Lambin P, Walsh S, Occchipinti M, Meunier P, Vos W, Lovinfosse P, Leijenaar RT. An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest CT. ERJ Open Res 2022; 8:00579-2021. [PMID: 35509437 PMCID: PMC8958945 DOI: 10.1183/23120541.00579-2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/04/2022] [Indexed: 01/08/2023] Open
Abstract
Purpose In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza. A fully automated artificial intelligence-based network is proposed to classify CT volumes of patients affected with COVID-19 or influenza/CAP, and in the uninfectedhttps://bit.ly/3MJrVRi
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Afshar P, Rafiee MJ, Naderkhani F, Heidarian S, Enshaei N, Oikonomou A, Babaki Fard F, Anconina R, Farahani K, Plataniotis KN, Mohammadi A. Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network. Sci Rep 2022; 12:4827. [PMID: 35318368 PMCID: PMC8940967 DOI: 10.1038/s41598-022-08796-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/01/2022] [Indexed: 01/01/2023] Open
Abstract
Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of \documentclass[12pt]{minimal}
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\begin{document}$$89.5\%\pm 0.11$$\end{document}89.5%±0.11, CAP sensitivity of \documentclass[12pt]{minimal}
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\begin{document}$$95\%\pm 0.11$$\end{document}95%±0.11, normal cases sensitivity (specificity) of \documentclass[12pt]{minimal}
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\begin{document}$$90\%\pm 0.06$$\end{document}90%±0.06. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of \documentclass[12pt]{minimal}
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\begin{document}$$94.3\%\pm 0.05$$\end{document}94.3%±0.05, CAP sensitivity of \documentclass[12pt]{minimal}
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\begin{document}$$96.7\%\pm 0.07$$\end{document}96.7%±0.07, normal cases sensitivity (specificity) of \documentclass[12pt]{minimal}
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\begin{document}$$91\%\pm 0.09$$\end{document}91%±0.09 , and accuracy of \documentclass[12pt]{minimal}
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\begin{document}$$94.1\%\pm 0.03$$\end{document}94.1%±0.03. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.
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Affiliation(s)
- Parnian Afshar
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada.,Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
| | - Moezedin Javad Rafiee
- Department of Medicine and Diagnostic Radiology, McGill University Health Center-Research Institute, Montreal, QC, Canada
| | - Farnoosh Naderkhani
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada
| | - Shahin Heidarian
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Nastaran Enshaei
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada
| | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | | | - Reut Anconina
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), Rockville, MD, USA
| | | | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada.
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Montalbo FJ. Truncating fined-tuned vision-based models to lightweight deployable diagnostic tools for SARS-CoV-2 infected chest X-rays and CT-scans. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:16411-16439. [PMID: 35261555 PMCID: PMC8893243 DOI: 10.1007/s11042-022-12484-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 10/05/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
In such a brief period, the recent coronavirus (COVID-19) already infected large populations worldwide. Diagnosing an infected individual requires a Real-Time Polymerase Chain Reaction (RT-PCR) test, which can become expensive and limited in most developing countries, making them rely on alternatives like Chest X-Rays (CXR) or Computerized Tomography (CT) scans. However, results from these imaging approaches radiated confusion for medical experts due to their similarities with other diseases like pneumonia. Other solutions based on Deep Convolutional Neural Network (DCNN) recently improved and automated the diagnosis of COVID-19 from CXRs and CT scans. However, upon examination, most proposed studies focused primarily on accuracy rather than deployment and reproduction, which may cause them to become difficult to reproduce and implement in locations with inadequate computing resources. Therefore, instead of focusing only on accuracy, this work investigated the effects of parameter reduction through a proposed truncation method and analyzed its effects. Various DCNNs had their architectures truncated, which retained only their initial core block, reducing their parameter sizes to <1 M. Once trained and validated, findings have shown that a DCNN with robust layer aggregations like the InceptionResNetV2 had less vulnerability to the adverse effects of the proposed truncation. The results also showed that from its full-length size of 55 M with 98.67% accuracy, the proposed truncation reduced its parameters to only 441 K and still attained an accuracy of 97.41%, outperforming other studies based on its size to performance ratio.
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Affiliation(s)
- Francis Jesmar Montalbo
- College of Informatics and Computing Sciences, Batangas State University, Rizal Avenue Extension, Batangas, Batangas City, Philippines
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Alyasseri ZAA, Al‐Betar MA, Doush IA, Awadallah MA, Abasi AK, Makhadmeh SN, Alomari OA, Abdulkareem KH, Adam A, Damasevicius R, Mohammed MA, Zitar RA. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. EXPERT SYSTEMS 2022; 39:e12759. [PMID: 34511689 PMCID: PMC8420483 DOI: 10.1111/exsy.12759] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 05/17/2021] [Accepted: 06/07/2021] [Indexed: 05/02/2023]
Abstract
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
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Affiliation(s)
- Zaid Abdi Alkareem Alyasseri
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
- ECE Department‐Faculty of EngineeringUniversity of KufaNajafIraq
| | - Mohammed Azmi Al‐Betar
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Information TechnologyAl‐Huson University College, Al‐Balqa Applied UniversityIrbidJordan
| | - Iyad Abu Doush
- Computing Department, College of Engineering and Applied SciencesAmerican University of KuwaitSalmiyaKuwait
- Computer Science DepartmentYarmouk UniversityIrbidJordan
| | - Mohammed A. Awadallah
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Computer ScienceAl‐Aqsa UniversityGazaPalestine
| | - Ammar Kamal Abasi
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Faculty of Information TechnologyMiddle East UniversityAmmanJordan
| | | | | | - Afzan Adam
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
| | | | - Mazin Abed Mohammed
- College of Computer Science and Information TechnologyUniversity of AnbarAnbarIraq
| | - Raed Abu Zitar
- Sorbonne Center of Artificial IntelligenceSorbonne University‐Abu DhabiAbu DhabiUnited Arab Emirates
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Enshaei N, Oikonomou A, Rafiee MJ, Afshar P, Heidarian S, Mohammadi A, Plataniotis KN, Naderkhani F. COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images. Sci Rep 2022; 12:3212. [PMID: 35217712 PMCID: PMC8881477 DOI: 10.1038/s41598-022-06854-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 01/21/2022] [Indexed: 11/09/2022] Open
Abstract
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.
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Affiliation(s)
- Nastaran Enshaei
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
| | - Moezedin Javad Rafiee
- Department of Medicine and Diagnostic Radiology, McGill University, Montreal, QC, Canada
| | - Parnian Afshar
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| | - Shahin Heidarian
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| | | | - Farnoosh Naderkhani
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
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Dialameh M, Hamzeh A, Rahmani H, Radmard AR, Dialameh S. Proposing a novel deep network for detecting COVID-19 based on chest images. Sci Rep 2022; 12:3116. [PMID: 35210447 PMCID: PMC8873454 DOI: 10.1038/s41598-022-06802-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 01/24/2022] [Indexed: 11/29/2022] Open
Abstract
The rapid outbreak of coronavirus threatens humans’ life all around the world. Due to the insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. To date, researchers have proposed several detection models based on chest imaging analysis, primarily based on deep neural networks; however, none of which could achieve a reliable and highly sensitive performance yet. Therefore, the nature of this study is primary epidemiological research that aims to overcome the limitations mentioned above by proposing a large-scale publicly available dataset of chest computed tomography scan (CT-scan) images consisting of more than 13k samples. Secondly, we propose a more sensitive deep neural networks model for CT-scan images of the lungs, providing a pixel-wise attention layer on top of the high-level features extracted from the network. Moreover, the proposed model is extended through a transfer learning approach for being applicable in the case of chest X-Ray (CXR) images. The proposed model and its extension have been trained and evaluated through several experiments. The inclusion criteria were patients with suspected PE and positive real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2. The exclusion criteria were negative or inconclusive RT-PCR and other chest CT indications. Our model achieves an AUC score of 0.886, significantly better than its closest competitor, whose AUC is 0.843. Moreover, the obtained results on another commonly-used benchmark show an AUC of 0.899, outperforming related models. Additionally, the sensitivity of our model is 0.858, while that of its closest competitor is 0.81, explaining the efficiency of pixel-wise attention strategy in detecting coronavirus. Our promising results and the efficiency of the models imply that the proposed models can be considered reliable tools for assisting doctors in detecting coronavirus.
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Affiliation(s)
- Maryam Dialameh
- Department of Computer Science, Shiraz University, Shiraz, Iran.
| | - Ali Hamzeh
- Department of Computer Science, Shiraz University, Shiraz, Iran
| | - Hossein Rahmani
- School of Computing and Communications, Lancaster University, Lancaster, UK
| | - Amir Reza Radmard
- Department of Radiology, Tehran University of Medical Sciences, Tehran, Iran
| | - Safoura Dialameh
- School of Paramedical Sciences, Bushehr University of Medical Sciences, Bushehr, Iran
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Aria M, Nourani E, Golzari Oskouei A. ADA-COVID: Adversarial Deep Domain Adaptation-Based Diagnosis of COVID-19 from Lung CT Scans Using Triplet Embeddings. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2564022. [PMID: 35154300 PMCID: PMC8826267 DOI: 10.1155/2022/2564022] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/08/2021] [Accepted: 01/07/2022] [Indexed: 12/12/2022]
Abstract
Rapid diagnosis of COVID-19 with high reliability is essential in the early stages. To this end, recent research often uses medical imaging combined with machine vision methods to diagnose COVID-19. However, the scarcity of medical images and the inherent differences in existing datasets that arise from different medical imaging tools, methods, and specialists may affect the generalization of machine learning-based methods. Also, most of these methods are trained and tested on the same dataset, reducing the generalizability and causing low reliability of the obtained model in real-world applications. This paper introduces an adversarial deep domain adaptation-based approach for diagnosing COVID-19 from lung CT scan images, termed ADA-COVID. Domain adaptation-based training process receives multiple datasets with different input domains to generate domain-invariant representations for medical images. Also, due to the excessive structural similarity of medical images compared to other image data in machine vision tasks, we use the triplet loss function to generate similar representations for samples of the same class (infected cases). The performance of ADA-COVID is evaluated and compared with other state-of-the-art COVID-19 diagnosis algorithms. The obtained results indicate that ADA-COVID achieves classification improvements of at least 3%, 20%, 20%, and 11% in accuracy, precision, recall, and F1 score, respectively, compared to the best results of competitors, even without directly training on the same data. The implementation source code of the ADA-COVID is publicly available at https://github.com/MehradAria/ADA-COVID.
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Affiliation(s)
- Mehrad Aria
- Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Esmaeil Nourani
- Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Amin Golzari Oskouei
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
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