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Rashid PQ, Türker İ. Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network. Diagnostics (Basel) 2024; 14:1313. [PMID: 38928728 PMCID: PMC11202625 DOI: 10.3390/diagnostics14121313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
Computed tomography (CT) scans have recently emerged as a major technique for the fast diagnosis of lung diseases via image classification techniques. In this study, we propose a method for the diagnosis of COVID-19 disease with improved accuracy by utilizing graph convolutional networks (GCN) at various layer formations and kernel sizes to extract features from CT scan images. We apply a U-Net model to aid in segmentation and feature extraction. In contrast with previous research retrieving deep features from convolutional filters and pooling layers, which fail to fully consider the spatial connectivity of the nodes, we employ GCNs for classification and prediction to capture spatial connectivity patterns, which provides a significant association benefit. We handle the extracted deep features to form an adjacency matrix that contains a graph structure and pass it to a GCN along with the original image graph and the largest kernel graph. We combine these graphs to form one block of the graph input and then pass it through a GCN with an additional dropout layer to avoid overfitting. Our findings show that the suggested framework, called the feature-extracted graph convolutional network (FGCN), performs better in identifying lung diseases compared to recently proposed deep learning architectures that are not based on graph representations. The proposed model also outperforms a variety of transfer learning models commonly used for medical diagnosis tasks, highlighting the abstraction potential of the graph representation over traditional methods.
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Affiliation(s)
| | - İlker Türker
- Department of Computer Engineering, Karabuk University, 78050 Karabuk, Turkey;
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2
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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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3
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Zhu R, Li J, Yang J, Sun R, Yu K. In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning. Animals (Basel) 2024; 14:628. [PMID: 38396595 PMCID: PMC10886402 DOI: 10.3390/ani14040628] [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: 01/15/2024] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consumption, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-ray images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective features at various stages. Next, the selected shape features were automatically extracted from the mask images. Finally, these features, including live weight, were input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35%) with the lowest parameter count (1.51 M) compared to the other segmentation models. The SVR model performed best (R2 = 0.8810) compared to the other prediction models in the five-fold cross-validation. The research findings can be applied to broiler production and breeding, reducing measurement costs, and enhancing breeding efficiency.
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Affiliation(s)
- Rui Zhu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; (R.Z.); (J.L.); (J.Y.)
| | - Jiayao Li
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; (R.Z.); (J.L.); (J.Y.)
| | - Junyan Yang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; (R.Z.); (J.L.); (J.Y.)
| | - Ruizhi Sun
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; (R.Z.); (J.L.); (J.Y.)
- Scientific Research Base for Integrated Technologies of Precision Agriculture (Animal Husbandry), The Ministry of Agriculture, Beijing 100083, China
| | - Kun Yu
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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4
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Gao F, Li B, Chen L, Wei X, Shang Z, Liu C. Ultrasound image super-resolution reconstruction based on semi-supervised CycleGAN. ULTRASONICS 2024; 137:107177. [PMID: 37832382 DOI: 10.1016/j.ultras.2023.107177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/31/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023]
Abstract
In ultrasonic testing, diffraction artifacts generated around defects increase the challenge of quantitatively characterizing defects. In this paper, we propose a label-enhanced semi-supervised CycleGAN network model, referred to as LESS-CycleGAN, which is a conditional cycle generative adversarial network designed for accurately characterizing defect morphology in ultrasonic testing images. The proposed method introduces paired cross-domain image samples during model training to achieve a defect transformation between the ultrasound image domain and the morphology image domain, thereby eliminating artifacts. Furthermore, the method incorporates a novel authenticity loss function to ensure high-precision defect reconstruction capability. To validate the effectiveness and robustness of the model, we use simulated 2D images of defects and corresponding ultrasonic detection images as training and test sets, and an actual ultrasonic phased array image of a test block as the validation set to evaluate the model's application performance. The experimental results demonstrate that the proposed method is convenient and effective, achieving subwavelength-scale defect reconstruction with good robustness.
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Affiliation(s)
- Fei Gao
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Bing Li
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Lei Chen
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Xiang Wei
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zhongyu Shang
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Chunman Liu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, 710049, China
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Singh K, Kaur N, Prabhu A. Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review. Curr Top Med Chem 2024; 24:737-753. [PMID: 38318824 DOI: 10.2174/0115680266282179240124072121] [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: 10/18/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 02/07/2024]
Abstract
BACKGROUND SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and therapeutic approaches. Artificial intelligence-based methods have contributed a significant part in tackling complicated issues, and some institutions have been quick to embrace and tailor these solutions in response to the COVID-19 pandemic's obstacles. Here, in this review article, we have covered a few DL techniques for COVID-19 detection and diagnosis, as well as ML techniques for COVID-19 identification, severity classification, vaccine and drug development, mortality rate prediction, contact tracing, risk assessment, and public distancing. This review illustrates the overall impact of AI/ML tools on tackling and managing the outbreak. PURPOSE The focus of this research was to undertake a thorough evaluation of the literature on the part of Artificial Intelligence (AI) as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development. METHODS A comprehensive exploration of PubMed, Web of Science, and Science Direct was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations to find all possibly suitable papers conducted and made publicly available between December 1, 2019, and August 2023. COVID-19, along with AI-specific words, was used to create the query syntax. RESULTS During the period covered by the search strategy, 961 articles were published and released online. Out of these, a total of 135 papers were chosen for additional investigation. Mortality rate prediction, early detection and diagnosis, vaccine as well as drug development, and lastly, incorporation of AI for supervising and controlling the COVID-19 pandemic were the four main topics focused entirely on AI applications used to tackle the COVID-19 crisis. Out of 135, 60 research papers focused on the detection and diagnosis of the COVID-19 pandemic. Next, 19 of the 135 studies applied a machine-learning approach for mortality rate prediction. Another 22 research publications emphasized the vaccine as well as drug development. Finally, the remaining studies were concentrated on controlling the COVID-19 pandemic by applying AI AI-based approach to it. CONCLUSION We compiled papers from the available COVID-19 literature that used AI-based methodologies to impart insights into various COVID-19 topics in this comprehensive study. Our results suggest crucial characteristics, data types, and COVID-19 tools that can aid in medical and translational research facilitation.
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Affiliation(s)
- Kavya Singh
- Department of Biotechnology, Banasthali University, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India
| | - Navjeet Kaur
- Department of Chemistry & Division of Research and Development, Lovely Professional University, Phagwara, 144411, Punjab, India
| | - Ashish Prabhu
- Biotechnology Department, NIT Warangal, Warangal, 506004, Telangana, India
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6
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Chato L, Regentova E. Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data. J Pers Med 2023; 13:1703. [PMID: 38138930 PMCID: PMC10744730 DOI: 10.3390/jpm13121703] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/01/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.
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Affiliation(s)
- Lina Chato
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA;
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7
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Nahiduzzaman M, Goni MOF, Hassan R, Islam MR, Syfullah MK, Shahriar SM, Anower MS, Ahsan M, Haider J, Kowalski M. Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19. EXPERT SYSTEMS WITH APPLICATIONS 2023; 229:120528. [PMID: 37274610 PMCID: PMC10223636 DOI: 10.1016/j.eswa.2023.120528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/19/2023] [Accepted: 05/19/2023] [Indexed: 06/06/2023]
Abstract
Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia have spread over the world, killing millions of people. Medical specialists have experienced challenges in correctly identifying these diseases due to their subtle differences in Chest X-ray images (CXR). To assist the medical experts, this study proposed a computer-aided lung illness identification method based on the CXR images. For the first time, 17 different forms of lung disorders were considered and the study was divided into six trials with each containing two, two, three, four, fourteen, and seventeen different forms of lung disorders. The proposed framework combined robust feature extraction capabilities of a lightweight parallel convolutional neural network (CNN) with the classification abilities of the extreme learning machine algorithm named CNN-ELM. An optimistic accuracy of 90.92% and an area under the curve (AUC) of 96.93% was achieved when 17 classes were classified side by side. It also accurately identified COVID-19 and TB with 99.37% and 99.98% accuracy, respectively, in 0.996 microseconds for a single image. Additionally, the current results also demonstrated that the framework could outperform the existing state-of-the-art (SOTA) models. On top of that, a secondary conclusion drawn from this study was that the prospective framework retained its effectiveness over a range of real-world environments, including balanced-unbalanced or large-small datasets, large multiclass or simple binary class, and high- or low-resolution images. A prototype Android App was also developed to establish the potential of the framework in real-life implementation.
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Affiliation(s)
- Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Omaer Faruq Goni
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Rakibul Hassan
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Robiul Islam
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Khalid Syfullah
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Saleh Mohammed Shahriar
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Shamim Anower
- Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK
| | - 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, 00-908 Warsaw, Poland
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8
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Hadi MU, Qureshi R, Ahmed A, Iftikhar N. A lightweight CORONA-NET for COVID-19 detection in X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 225:120023. [PMID: 37063778 PMCID: PMC10088342 DOI: 10.1016/j.eswa.2023.120023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 06/19/2023]
Abstract
Since December 2019, COVID-19 has posed the most serious threat to living beings. With the advancement of vaccination programs around the globe, the need to quickly diagnose COVID-19 in general with little logistics is fore important. As a consequence, the fastest diagnostic option to stop COVID-19 from spreading, especially among senior patients, should be the development of an automated detection system. This study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (CNN), discrete wavelet transform (DWT), and a long short-term memory (LSTM), called CORONA-NET for diagnosing COVID-19 from chest X-ray images. In this system, deep feature extraction is performed by CNN, the feature vector is reduced yet strengthened by DWT, and the extracted feature is detected by LSTM for prediction. The dataset included 3000 X-rays, 1000 of which were COVID-19 obtained locally. Within minutes of the test, the proposed test platform's prototype can accurately detect COVID-19 patients. The proposed method achieves state-of-the-art performance in comparison with the existing deep learning methods. We hope that the suggested method will hasten clinical diagnosis and may be used for patients in remote areas where clinical labs are not easily accessible due to a lack of resources, location, or other factors.
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Affiliation(s)
- Muhammad Usman Hadi
- Nanotechnology and Integrated Bio-Engineering Centre (NIBEC), School of Engineering, Ulster University, BT15 1AP Belfast, UK
| | - Rizwan Qureshi
- Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, TX 77030, USA
| | - Ayesha Ahmed
- Department of Radiology, Aalborg University Hospital, Aalborg 9000, Denmark
| | - Nadeem Iftikhar
- University College of Northern Denmark, Aalborg 9200, Denmark
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John P, Vasa NJ, Zam A. Optical Biosensors for the Diagnosis of COVID-19 and Other Viruses-A Review. Diagnostics (Basel) 2023; 13:2418. [PMID: 37510162 PMCID: PMC10378272 DOI: 10.3390/diagnostics13142418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/12/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
The sudden outbreak of the COVID-19 pandemic led to a huge concern globally because of the astounding increase in mortality rates worldwide. The medical imaging computed tomography technique, whole-genome sequencing, and electron microscopy are the methods generally used for the screening and identification of the SARS-CoV-2 virus. The main aim of this review is to emphasize the capabilities of various optical techniques to facilitate not only the timely and effective diagnosis of the virus but also to apply its potential toward therapy in the field of virology. This review paper categorizes the potential optical biosensors into the three main categories, spectroscopic-, nanomaterial-, and interferometry-based approaches, used for detecting various types of viruses, including SARS-CoV-2. Various classifications of spectroscopic techniques such as Raman spectroscopy, near-infrared spectroscopy, and fluorescence spectroscopy are discussed in the first part. The second aspect highlights advances related to nanomaterial-based optical biosensors, while the third part describes various optical interferometric biosensors used for the detection of viruses. The tremendous progress made by lab-on-a-chip technology in conjunction with smartphones for improving the point-of-care and portability features of the optical biosensors is also discussed. Finally, the review discusses the emergence of artificial intelligence and its applications in the field of bio-photonics and medical imaging for the diagnosis of COVID-19. The review concludes by providing insights into the future perspectives of optical techniques in the effective diagnosis of viruses.
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Affiliation(s)
- Pauline John
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi 129188, United Arab Emirates
| | - Nilesh J Vasa
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai 600036, India
| | - Azhar Zam
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi 129188, United Arab Emirates
- Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA
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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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AlMohimeed A, Saleh H, El-Rashidy N, Saad RMA, El-Sappagh S, Mostafa S. Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13111968. [PMID: 37296820 DOI: 10.3390/diagnostics13111968] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset.
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Affiliation(s)
- Abdulaziz AlMohimeed
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
| | - Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
| | - Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Redhwan M A Saad
- College of Informatics, Midocean University, Moroni 8722, Comoros
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Sherif Mostafa
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
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12
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Poola RG, Pl L, Y SS. COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm. RESULTS IN ENGINEERING 2023; 18:101020. [PMID: 36945336 PMCID: PMC10017171 DOI: 10.1016/j.rineng.2023.101020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 03/01/2023] [Accepted: 03/08/2023] [Indexed: 05/14/2023]
Abstract
Due to the augmented rise of COVID-19, clinical specialists are looking for fast faultless diagnosis strategies to restrict Covid spread while attempting to lessen the computational complexity. In this way, swift diagnosis techniques for COVID-19 with high precision can offer valuable aid to clinical specialists. RT- PCR test is an expensive and tedious COVID diagnosis technique in practice. Medical imaging is feasible to diagnose COVID-19 by X-ray chest radiography to get around the shortcomings of RT-PCR. Through a variety of Deep Transfer-learning models, this research investigates the potential of Artificial Intelligence -based early diagnosis of COVID-19 via X-ray chest radiographs. With 10,192 normal and 3616 Covid X-ray chest radiographs, the deep transfer-learning models are optimized to further the accurate diagnosis. The x-ray chest radiographs undergo a data augmentation phase before developing a modified dataset to train the Deep Transfer-learning models. The Deep Transfer-learning architectures are trained using the extracted features from the Feature Extraction stage. During training, the classification of X-ray Chest radiographs based on feature extraction algorithm values is converted into a feature label set containing the classified image data with a feature string value representing the number of edges detected after edge detection. The feature label set is further tested with the SVM, KNN, NN, Naive Bayes and Logistic Regression classifiers to audit the quality metrics of the proposed model. The quality metrics include accuracy, precision, F1 score, recall and AUC. The Inception-V3 dominates the six Deep Transfer-learning models, according to the assessment results, with a training accuracy of 84.79% and a loss function of 2.4%. The performance of Cubic SVM was superior to that of the other SVM classifiers, with an AUC score of 0.99, precision of 0.983, recall of 0.8977, accuracy of 95.8%, and F1 score of 0.9384. Cosine KNN fared better than the other KNN classifiers with an AUC score of 0.95, precision of 0.974, recall of 0.777, accuracy of 90.8%, and F1 score of 0.864. Wide NN fared better than the other NN classifiers with an AUC score of 0.98, precision of 0.975, recall of 0.907, accuracy of 95.5%, and F1 score of 0.939. According to the findings, SVM classifiers topped other classifiers in terms of performance indicators like accuracy, precision, recall, F1-score, and AUC. The SVM classifiers reported better mean optimal scores compared to other classifiers. The performance assessment metrics uncover that the proposed methodology can aid in preliminary COVID diagnosis.
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Affiliation(s)
| | - Lahari Pl
- Dept. of ECE, SRM University, AP, India
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13
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Subramaniam K, Palanisamy N, Sinnaswamy RA, Muthusamy S, Mishra OP, Loganathan AK, Ramamoorthi P, Gnanakkan CARC, Thangavel G, Sundararajan SCM. A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques. Soft comput 2023; 27:1-22. [PMID: 37362273 PMCID: PMC10220331 DOI: 10.1007/s00500-023-08561-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2023] [Indexed: 06/28/2023]
Abstract
COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization.
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Affiliation(s)
- Kavitha Subramaniam
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu India
| | - Natesan Palanisamy
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu India
| | - Renugadevi Ammapalayam Sinnaswamy
- Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu India
| | - Suresh Muthusamy
- Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu India
| | - Om Prava Mishra
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu India
| | - Ashok Kumar Loganathan
- Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamil Nadu India
| | - Ponarun Ramamoorthi
- Department of Electrical and Electronics Engineering, Theni Kammavar Sangam College of Technology, Theni, Tamil Nadu India
| | | | - Gunasekaran Thangavel
- Department of Engineering, University of Technology and Applied Sciences, Muscat, Sultanate of Oman
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Shareef AQ, Kurnaz S. Deep Learning Based COVID-19 Detection via Hard Voting Ensemble Method. WIRELESS PERSONAL COMMUNICATIONS 2023:1-12. [PMID: 37360134 PMCID: PMC10170044 DOI: 10.1007/s11277-023-10485-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/24/2023] [Indexed: 06/28/2023]
Abstract
Healthcare systems throughout the world are under a great deal of strain because to the continuing COVID-19 epidemic, making early and precise diagnosis critical for limiting the virus's propagation and efficiently treating victims. The utilization of medical imaging methods like X-rays can help to speed up the diagnosis procedure. Which can offer valuable insights into the virus's existence in the lungs. We present a unique ensemble approach to identify COVID-19 using X-ray pictures (X-ray-PIC) in this paper. The suggested approach, based on hard voting, combines the confidence scores of three classic deep learning models: CNN, VGG16, and DenseNet. We also apply transfer learning to enhance performance on small medical image datasets. Experiments indicate that the suggested strategy outperforms current techniques with a 97% accuracy, a 96% precision, a 100% recall, and a 98% F1-score.These results demonstrate the effectiveness of using ensemble approaches and COVID-19 transfer-learning diagnosis using X-ray-PIC, which could greatly aid in early detection and reducing the burden on global health systems.
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Affiliation(s)
- Asaad Qasim Shareef
- Department of Electrical Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Sefer Kurnaz
- Department of Electrical Computer Engineering, Altinbas University, Istanbul, Turkey
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15
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Dabbagh R, Jamal A, Bhuiyan Masud JH, Titi MA, Amer YS, Khayat A, Alhazmi TS, Hneiny L, Baothman FA, Alkubeyyer M, Khan SA, Temsah MH. Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review. Cureus 2023; 15:e38373. [PMID: 37265897 PMCID: PMC10230599 DOI: 10.7759/cureus.38373] [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] [Accepted: 04/30/2023] [Indexed: 06/03/2023] Open
Abstract
During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.
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Affiliation(s)
- Rufaidah Dabbagh
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Amr Jamal
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | | | - Maher A Titi
- Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Yasser S Amer
- Pediatrics, Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Afnan Khayat
- Health Information Management Department, Prince Sultan Military College of Health Sciences, Al Dhahran, SAU
| | - Taha S Alhazmi
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Layal Hneiny
- Medicine, Wegner Health Sciences Library, University of South Dakota, Vermillion, USA
| | - Fatmah A Baothman
- Department of Information Systems, King Abdulaziz University, Jeddah, SAU
| | | | - Samina A Khan
- School of Computer Sciences, Universiti Sains Malaysia, Penang, MYS
| | - Mohamad-Hani Temsah
- Pediatric Intensive Care Unit, Department of Pediatrics, King Saud University, Riyadh, SAU
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Khattab R, Abdelmaksoud IR, Abdelrazek S. Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey. NEW GENERATION COMPUTING 2023; 41:343-400. [PMID: 37229176 PMCID: PMC10071474 DOI: 10.1007/s00354-023-00213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 02/23/2023] [Indexed: 05/27/2023]
Abstract
Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.
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Affiliation(s)
- Rana Khattab
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Islam R. Abdelmaksoud
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Samir Abdelrazek
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
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Challenges, opportunities, and advances related to COVID-19 classification based on deep learning. DATA SCIENCE AND MANAGEMENT 2023. [PMCID: PMC10063459 DOI: 10.1016/j.dsm.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities: - computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches is highlighted a future research possibility.
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Bhosale YH, Patnaik KS. Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-54. [PMID: 37362676 PMCID: PMC10015538 DOI: 10.1007/s11042-023-15029-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 02/01/2023] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
This review investigates how Deep Machine Learning (DML) has dealt with the Covid-19 epidemic and provides recommendations for future Covid-19 research. Despite the fact that vaccines for this epidemic have been developed, DL methods have proven to be a valuable asset in radiologists' arsenals for the automated assessment of Covid-19. This detailed review debates the techniques and applications developed for Covid-19 findings using DL systems. It also provides insights into notable datasets used to train neural networks, data partitioning, and various performance measurement metrics. The PRISMA taxonomy has been formed based on pretrained(45 systems) and hybrid/custom(17 systems) models with radiography modalities. A total of 62 systems with respect to X-ray(32), CT(19), ultrasound(7), ECG(2), and genome sequence(2) based modalities as taxonomy are selected from the studied articles. We originate by valuing the present phase of DL and conclude with significant limitations. The restrictions contain incomprehensibility, simplification measures, learning from incomplete labeled data, and data secrecy. Moreover, DML can be utilized to detect and classify Covid-19 from other COPD illnesses. The proposed literature review has found many DL-based systems to fight against Covid19. We expect this article will assist in speeding up the procedure of DL for Covid-19 researchers, including medical, radiology technicians, and data engineers.
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Affiliation(s)
- Yogesh H. Bhosale
- Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India
| | - K. Sridhar Patnaik
- Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India
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19
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Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning. BENCHCOUNCIL TRANSACTIONS ON BENCHMARKS, STANDARDS AND EVALUATIONS 2023:100088. [PMCID: PMC10010001 DOI: 10.1016/j.tbench.2023.100088] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques are more effective than the reverse-transcription polymerase chain reaction (RT-PCR) method in detecting coronavirus. Due to the limited availability of medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 patients from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2, where CNN is used to extract complex features from samples and classify them using RNN. In our experiments, the VGG19-RNN architecture outperformed all other networks in terms of accuracy. Finally, decision-making regions of images were visualized using gradient-weighted class activation mapping (Grad-CAM). The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff. All the data used during the study are openly available from the Mendeley data repository at https://data.mendeley.com/datasets/mxc6vb7svm. For further research, we have made the source code publicly available at https://github.com/Asraf047/COVID19-CNN-RNN.
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PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates. Biomed Signal Process Control 2023; 81:104445. [PMID: 36466567 PMCID: PMC9708623 DOI: 10.1016/j.bspc.2022.104445] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 10/10/2022] [Accepted: 11/20/2022] [Indexed: 12/05/2022]
Abstract
Background and Objective In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures. Methods Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several transfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes. Results PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases. Conclusion The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.
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Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans. INTELLIGENT SYSTEMS WITH APPLICATIONS 2023; 17:200160. [PMCID: PMC9708108 DOI: 10.1016/j.iswa.2022.200160] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/21/2022] [Accepted: 11/27/2022] [Indexed: 10/27/2023]
Abstract
COVID pandemic across the world and the emergence of new variants have intensified the need to identify COVID-19 cases quickly and efficiently. In this paper, a novel dual-mode multi-modal approach is presented to detect a covid patient. This has been done using the combination of image of the chest X-ray/CT scan and the clinical notes provided with the scan. Data augmentation techniques are used to extrapolate the dataset. Five different types of image and text models have been employed, including transfer learning. The binary cross entropy loss function and the adam optimizer are used to compile all of these models. The multi-modal is also tried out with existing pre-trained models such as: VGG16, ResNet50, InceptionResNetV2 and MobileNetV2. The final multi-modal gives an accuracy of 97.8% on the testing data. The study provides a different approach to identifying COVID-19 cases using just the scan images and the corresponding notes.
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Kanjanasurat I, Tenghongsakul K, Purahong B, Lasakul A. CNN-RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images. SENSORS (BASEL, SWITZERLAND) 2023; 23:1356. [PMID: 36772394 PMCID: PMC9919640 DOI: 10.3390/s23031356] [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: 11/26/2022] [Revised: 01/07/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images are capable of accurately diagnosing COVID-19. To distinguish lung illnesses (i.e., COVID-19 and pneumonia) from normal cases using chest X-ray and CT images, we combined convolutional neural network (CNN) and recurrent neural network (RNN) models by replacing the fully connected layers of CNN with a version of RNN. In this framework, the attributes of CNNs were utilized to extract features and those of RNNs to calculate dependencies and classification base on extracted features. CNN models VGG19, ResNet152V2, and DenseNet121 were combined with long short-term memory (LSTM) and gated recurrent unit (GRU) RNN models, which are convenient to develop because these networks are all available as features on many platforms. The proposed method is evaluated using a large dataset totaling 16,210 X-ray and CT images (5252 COVID-19 images, 6154 pneumonia images, and 4804 normal images) were taken from several databases, which had various image sizes, brightness levels, and viewing angles. Their image quality was enhanced via normalization, gamma correction, and contrast-limited adaptive histogram equalization. The ResNet152V2 with GRU model achieved the best architecture with an accuracy of 93.37%, an F1 score of 93.54%, a precision of 93.73%, and a recall of 93.47%. From the experimental results, the proposed method is highly effective in distinguishing lung diseases. Furthermore, both CT and X-ray images can be used as input for classification, allowing for the rapid and easy detection of COVID-19.
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Affiliation(s)
| | - Kasi Tenghongsakul
- School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Boonchana Purahong
- School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Attasit Lasakul
- School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
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Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images. Neural Comput Appl 2023; 35:9637-9655. [PMID: 36714075 PMCID: PMC9873217 DOI: 10.1007/s00521-023-08200-0] [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: 01/28/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023]
Abstract
The new COVID-19 emerged in a town in China named Wuhan in December 2019, and since then, this deadly virus has infected 324 million people worldwide and caused 5.53 million deaths by January 2022. Because of the rapid spread of this pandemic, different countries are facing the problem of a shortage of resources, such as medical test kits and ventilators, as the number of cases increased uncontrollably. Therefore, developing a readily available, low-priced, and automated approach for COVID-19 identification is the need of the hour. The proposed study uses chest radiography images (CRIs) such as X-rays and computed tomography (CTs) to detect chest infections, as these modalities contain important information about chest infections. This research introduces a novel hybrid deep learning model named Lightweight ResGRU that uses residual blocks and a bidirectional gated recurrent unit to diagnose non-COVID and COVID-19 infections using pre-processed CRIs. Lightweight ResGRU is used for multi-modal two-class classification (normal and COVID-19), three-class classification (normal, COVID-19, and viral pneumonia), four-class classification (normal, COVID-19, viral pneumonia, and bacterial pneumonia), and COVID-19 severity types' classification (i.e., atypical appearance, indeterminate appearance, typical appearance, and negative for pneumonia). The proposed architecture achieved f-measure of 99.0%, 98.4%, 91.0%, and 80.5% for two-class, three-class, four-class, and COVID-19 severity level classifications, respectively, on unseen data. A large dataset is created by combining and changing different publicly available datasets. The results prove that radiologists can adopt this method to screen chest infections where test kits are limited.
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24
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Khan A, Akram MU, Nazir S. Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization. PLoS One 2023; 18:e0280352. [PMID: 36649367 PMCID: PMC9844910 DOI: 10.1371/journal.pone.0280352] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/27/2022] [Indexed: 01/18/2023] Open
Abstract
Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset.
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Affiliation(s)
- Asad Khan
- Computer and Software Engineering Department, National University of Sciences and Technology, Islamabad, Pakistan
| | - Muhammad Usman Akram
- Computer and Software Engineering Department, National University of Sciences and Technology, Islamabad, Pakistan
| | - Sajid Nazir
- Department of Computing, Glasgow Caledonian University, Glasgow, United Kingdom
- * E-mail:
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Moujahid H, Cherradi B, El Gannour O, Nagmeldin W, Abdelmaboud A, Al-Sarem M, Bahatti L, Saeed F, Hadwan M. A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images. COMPUTER SYSTEMS SCIENCE AND ENGINEERING 2023; 46:1789-1809. [DOI: 10.32604/csse.2023.034022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/13/2022] [Indexed: 06/15/2023]
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26
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Cao Z, Huang J, He X, Zong Z. BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images. Knowl Based Syst 2022; 258:110040. [PMID: 36284666 PMCID: PMC9585896 DOI: 10.1016/j.knosys.2022.110040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/09/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
During the past two years, a highly infectious virus known as COVID-19 has been damaging and harming the health of people all over the world. Simultaneously, the number of patients is rising in various countries, with many new cases appearing daily, posing a significant challenge to hospital medical staff. It is necessary to improve the efficiency of virus detection. To this end, we combine modern technology and visual assistance to detect COVID-19. Based on the above facts, for accurate and rapid identification of infected persons, the BND-VGG-19 method was proposed. This method is based on VGG-19 and further incorporates batch normalization and dropout layers between the layers to improve network accuracy. Then, the COVID-19 dataset including viral pneumonia, COVID-19, and normal X-ray images, are used to diagnose lung abnormalities and test the performance of the proposed algorithm. The experimental results show the superiority of BND-VGG-19 with a 95.48% accuracy rate compared with existing COVID-19 diagnostic methods.
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Affiliation(s)
- Zili Cao
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China
| | - Junjian Huang
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China,Corresponding author
| | - Xing He
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China
| | - Zhaowen Zong
- Department of Training Base for Health Care, Army Medical University, Chongqing 400038, PR China
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27
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Zeng Q, Feng Z, Zhu Y, Zhang Y, Shu X, Wu A, Luo L, Cao Y, Xiong J, Li H, Zhou F, Jie Z, Tu Y, Li Z. Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images. Front Oncol 2022; 12:1065934. [PMID: 36531076 PMCID: PMC9748811 DOI: 10.3389/fonc.2022.1065934] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 11/07/2022] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images. METHODS We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance. RESULTS The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC. CONCLUSIONS These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.
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Affiliation(s)
- Qingwen Zeng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zongfeng Feng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yang Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xufeng Shu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Ahao Wu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Lianghua Luo
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yi Cao
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Jianbo Xiong
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Hong Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Zhigang Jie
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Zhengrong Li
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
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T N SG, Satish R, Sridhar R. Learning effective embedding for automated COVID-19 prediction from chest X-ray images. MULTIMEDIA SYSTEMS 2022; 29:739-751. [PMID: 36310764 PMCID: PMC9596346 DOI: 10.1007/s00530-022-01015-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
The pandemic that the SARS-CoV-2 originated in 2019 is continuing to cause serious havoc on the global population's health, economy, and livelihood. A critical way to suppress and restrain this pandemic is the early detection of COVID-19, which will help to control the virus. Chest X-rays are one of the more straightforward ways to detect the COVID-19 virus compared to the standard methods like CT scans and RT-PCR diagnosis, which are very complex, expensive, and take much time. Our research on various papers shows that the currently researchers are actively working for an efficient Deep Learning model to produce an unbiased detection of COVID-19 through chest X-ray images. In this work, we propose a novel convolution neural network model based on supervised classification that simultaneously computes identification and verification loss. We adopt a transfer learning approach using pretrained models trained on imagenet dataset such as Alex Net and VGG16 as back-bone models and use data augmentation techniques to solve class imbalance and boost the classifier's performance. Finally, our proposed classifier architecture model ensures unbiased and high accuracy results, outperforming existing deep learning models for COVID-19 detection from chest X-ray images producing State of the Art performance. It shows strong and robust performance and proves to be easily deployable and scalable, therefore increasing the efficiency of analyzing chest X-ray images with high accuracy in detection of Coronavirus.
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Affiliation(s)
- Sree Ganesh T N
- Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015 India
| | - Rishi Satish
- Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015 India
| | - Rajeswari Sridhar
- Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015 India
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29
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Zhang H, Ma R, Zhao Y, Zhang Q, Sun Q, Ma Y. Optimized Convolutional Neural Network Recognition for Athletes' Pneumonia Image Based on Attention Mechanism. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1434. [PMID: 37420452 DOI: 10.3390/e24101434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/20/2022] [Accepted: 09/20/2022] [Indexed: 07/09/2023]
Abstract
After high-intensity exercise, athletes have a greatly increased possibility of pneumonia infection due to the immune function of athletes decreasing. Diseases caused by pulmonary bacterial or viral infections can have serious consequences on the health of athletes in a short period of time, and can even lead to their early retirement. Therefore, early diagnosis is the key to athletes' early recovery from pneumonia. Existing identification methods rely too much on professional medical knowledge, which leads to inefficient diagnosis due to the shortage of medical staff. To solve this problem, this paper presents an optimized convolutional neural network recognition method based on an attention mechanism after image enhancement. For the collected images of athlete pneumonia, we first use contrast boost to adjust the coefficient distribution. Then, the edge coefficient is extracted and enhanced to highlight the edge information, and enhanced images of the athlete lungs are obtained by using the inverse curvelet transformation. Finally, an optimized convolutional neural network with an attention mechanism is used to identify the athlete lung images. A series of experimental results show that, compared with the typical image recognition methods based on DecisionTree and RandomForest, the proposed method has higher recognition accuracy for lung images.
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Affiliation(s)
- Hui Zhang
- College of Physical, Henan Normal University, Xinxiang 453007, China
| | - Ruipu Ma
- College of Software, Henan Normal University, Xinxiang 453007, China
| | - Yingao Zhao
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Qianqian Zhang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Quandang Sun
- College of Software, Henan Normal University, Xinxiang 453007, China
| | - Yuanyuan Ma
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
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30
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Rout AK, Sethy A, Nayak SR. Adaptive MLELM-AE model for efficient prediction of stock market data. JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS 2022. [DOI: 10.1080/09720510.2022.2130567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Ajit Kumar Rout
- Department of Information Technology, GMR Institute of Technology, Srikakulam, Andhra Pradesh, India
| | - Abhisek Sethy
- Department of Information Technology, GMR Institute of Technology, Srikakulam, Andhra Pradesh, India
| | - Soumya Ranjan Nayak
- Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India
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31
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Karthik R, Menaka R, Hariharan M, Kathiresan GS. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. Ing Rech Biomed 2022; 43:486-510. [PMID: 34336141 PMCID: PMC8312058 DOI: 10.1016/j.irbm.2021.07.002] [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: 02/15/2021] [Revised: 06/14/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - M Hariharan
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India
| | - G S Kathiresan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
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32
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Costa YMG, Silva SA, Teixeira LO, Pereira RM, Bertolini D, Britto AS, Oliveira LS, Cavalcanti GDC. COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. SENSORS (BASEL, SWITZERLAND) 2022; 22:7303. [PMID: 36236402 PMCID: PMC9570662 DOI: 10.3390/s22197303] [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: 08/09/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.
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Affiliation(s)
- Yandre M. G. Costa
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Sergio A. Silva
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Lucas O. Teixeira
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | | | - Diego Bertolini
- Departamento Acadêmico de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Campo Mourão 87301-899, Brazil
| | - Alceu S. Britto
- Departmento de Ciência da Computação, Pontifícia Universidade Católica do Paraná, Curitiba 80215-901, Brazil
| | - Luiz S. Oliveira
- Departamento de Informática, Universidade Federal do Paraná, Curitiba 81531-980, Brazil
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Bhosale YH, Patnaik KS. Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review. Neural Process Lett 2022; 55:1-53. [PMID: 36158520 PMCID: PMC9483290 DOI: 10.1007/s11063-022-11023-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2022] [Indexed: 01/09/2023]
Abstract
Covid-19 is now one of the most incredibly intense and severe illnesses of the twentieth century. Covid-19 has already endangered the lives of millions of people worldwide due to its acute pulmonary effects. Image-based diagnostic techniques like X-ray, CT, and ultrasound are commonly employed to get a quick and reliable clinical condition. Covid-19 identification out of such clinical scans is exceedingly time-consuming, labor-intensive, and susceptible to silly intervention. As a result, radiography imaging approaches using Deep Learning (DL) are consistently employed to achieve great results. Various artificial intelligence-based systems have been developed for the early prediction of coronavirus using radiography pictures. Specific DL methods such as CNN and RNN noticeably extract extremely critical characteristics, primarily in diagnostic imaging. Recent coronavirus studies have used these techniques to utilize radiography image scans significantly. The disease, as well as the present pandemic, was studied using public and private data. A total of 64 pre-trained and custom DL models concerning imaging modality as taxonomies are selected from the studied articles. The constraints relevant to DL-based techniques are the sample selection, network architecture, training with minimal annotated database, and security issues. This includes evaluating causal agents, pathophysiology, immunological reactions, and epidemiological illness. DL-based Covid-19 detection systems are the key focus of this review article. Covid-19 work is intended to be accelerated as a result of this study.
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Affiliation(s)
- Yogesh H. Bhosale
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215 India
| | - K. Sridhar Patnaik
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215 India
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34
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Tiwari A, Tripathi S, Pandey DC, Sharma N, Sharma S. Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach. Technol Health Care 2022; 30:1273-1286. [PMID: 36093719 DOI: 10.3233/thc-220114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment. OBJECTIVE The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images. METHODS The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results. RESULT The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques. CONCLUSION The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection.
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Affiliation(s)
- Alok Tiwari
- School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.,School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Sumit Tripathi
- Department of Electronics and Communication Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.,School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Dinesh Chandra Pandey
- Department of Management Studies, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | - Neeraj Sharma
- School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Shiru Sharma
- School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
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35
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Gupta V, Jain N, Sachdeva J, Gupta M, Mohan S, Bajuri MY, Ahmadian A. Improved COVID-19 detection with chest x-ray images using deep learning. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:37657-37680. [PMID: 35968409 PMCID: PMC9361266 DOI: 10.1007/s11042-022-13509-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 10/18/2021] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively.
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Affiliation(s)
- Vedika Gupta
- Jindal Global Business School, O.P. Jindal Global University, Haryana, India
| | - Nikita Jain
- Bharati Vidyapeeth’s College of Engineering, Delhi, India
| | - Jatin Sachdeva
- Bharati Vidyapeeth’s College of Engineering, Delhi, India
| | - Mudit Gupta
- Bharati Vidyapeeth’s College of Engineering, Delhi, India
| | - Senthilkumar Mohan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Mohd Yazid Bajuri
- Department of Orthopaedics and Traumatology, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, Malaysia
| | - Ali Ahmadian
- Decision Lab, Mediterranea University of Reggio Calabria, 89124 Reggio Calabria, Italy
- Department of Mathematics, Near East University, Nicosia, TRNC, Mersin 10, Turkey
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36
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Siddiqui S, Arifeen M, Hopgood A, Good A, Gegov A, Hossain E, Rahman W, Hossain S, Al Jannat S, Ferdous R, Masum S. Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review. SN COMPUTER SCIENCE 2022; 3:397. [PMID: 35911439 PMCID: PMC9312319 DOI: 10.1007/s42979-022-01326-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/11/2022] [Indexed: 10/29/2022]
Abstract
COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.
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Affiliation(s)
- Shah Siddiqui
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK.,School of Computing, University of Portsmouth (UoP), Lion Terrace, Portsmouth, PO1 3HE UK
| | - Murshedul Arifeen
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Adrian Hopgood
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Alice Good
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Alexander Gegov
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Elias Hossain
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Wahidur Rahman
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Shazzad Hossain
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Sabila Al Jannat
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Rezowan Ferdous
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Shamsul Masum
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
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Asif S, Wenhui Y, Amjad K, Jin H, Tao Y, Jinhai S. Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning. EXPERT SYSTEMS 2022; 40:EXSY13099. [PMID: 35945966 PMCID: PMC9353436 DOI: 10.1111/exsy.13099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 05/26/2022] [Accepted: 06/30/2022] [Indexed: 05/27/2023]
Abstract
Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.
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Affiliation(s)
- Sohaib Asif
- Key Laboratory for Information Photonic Technology of Shaanxi Province and Key Laboratory for Physical Electronics and Devices of the Ministry of Education, School of Electronic Science and Engineering, Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'anShaanxiChina
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Yi Wenhui
- Key Laboratory for Information Photonic Technology of Shaanxi Province and Key Laboratory for Physical Electronics and Devices of the Ministry of Education, School of Electronic Science and Engineering, Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'anShaanxiChina
| | - Kamran Amjad
- Key Laboratory for Information Photonic Technology of Shaanxi Province and Key Laboratory for Physical Electronics and Devices of the Ministry of Education, School of Electronic Science and Engineering, Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'anShaanxiChina
| | - Hou Jin
- School of Basic Medical ScienceXi'an Medical UniversityXi'anChina
| | - Yi Tao
- School of Computer Science and EngineeringXi'an Jiaotong UniversityXi'anChina
| | - Si Jinhai
- Key Laboratory for Information Photonic Technology of Shaanxi Province and Key Laboratory for Physical Electronics and Devices of the Ministry of Education, School of Electronic Science and Engineering, Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'anShaanxiChina
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38
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Sasikaladevi N. Delaunay triangulation based intelligent system for the diagnosis of covid from the low radiation CXR images. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:1-10. [PMID: 37360780 PMCID: PMC10112999 DOI: 10.1007/s12652-022-04329-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 07/11/2022] [Indexed: 06/28/2023]
Abstract
Covid-19 is a viral infection that causes a profound impact on the lives of the World population. It is a global pandemic spreading across the world in a faster way. It made a global impact on the health, economy, and education system in all the countries. As it is a rapidly spreading disease, prevention demands a fast and accurate diagnosis system. In a highly densely populated country, the demand for fast and affordable early diagnosis is required to reduce the disaster. Within this diagnosis time, the infection spreads rapidly and worsens the infected person's status. To provide a faster and more affordable early diagnosis of covid, posterior-anterior chest radiographs (CXR) are used. Diagnosis of covid from CXR is challenging due to the images' interclass similarity and intraclass variation. This study proposes a deep learning-based robust early diagnosis method for covid. To balance the intraclass variation and interclass similarity in CXR images, the deep fused Delaunay triangulation (DT) is proposed as the CXR has low radiation and unbalanced quality images. The deep features are to be extracted to increase the robustness of the diagnosis method. Without segmentation, the proposed DT algorithm achieves the accurate visualization of the suspicious region in the CXR. The proposed model is trained and tested by the largest benchmark covid-19 radiology dataset with 3616 covid CXR images and 3500 standard CXR images. The performance of the proposed system is analyzed in terms of accuracy, sensitivity, specificity, and AUC. The proposed system yields the highest validation accuracy.
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Affiliation(s)
- N. Sasikaladevi
- Dept. of CSE, School of Computing, SASTRA Deemed University, Thanjavur, TN India
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39
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Gupta RK, Kunhare N, Pathik N, Pathik B. An AI-enabled pre-trained model-based Covid detection model using chest X-ray images. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:37351-37377. [PMID: 35844979 PMCID: PMC9273923 DOI: 10.1007/s11042-021-11580-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 08/25/2021] [Accepted: 09/20/2021] [Indexed: 06/15/2023]
Abstract
The year 2020 and 2021 was the witness of Covid 19 and it was the leading cause of death throughout the world during this time period. It has an impact on a large geographic area, particularly in countries with a large population. Due to the fact that this novel coronavirus has been detected in all countries around the world, the World Health Organization (WHO) has declared Covid-19 to be a pandemic. This novel coronavirus spread quickly from person to person through the saliva droplets and direct or indirect contact with an infected person. The tests carried out to detect the Covid-19 are time-consuming and the primary cause of rapid growth in Covid19 cases. Early detection of Covid patient can play a significant role in controlling the Covid chain by isolation the patient and proper treatment at the right time. Recent research on Covid-19 claim that Chest CT and X-ray images can be used as the preliminary screening for Covid-19 detection. This paper suggested an Artificial Intelligence (AI) based approach for detecting Covid-19 by using X-ray and CT scan images. Due to the availability of the small Covid dataset, we are using a pre-trained model. In this paper, four pre-trained models named VGGNet-19, ResNet50, InceptionResNetV2 and MobileNet are trained to classify the X-ray images into the Covid and Normal classes. A model is tuned in such a way that a smaller percentage of Covid cases will be classified as Normal cases by employing normalization and regularization techniques. The updated binary cross entropy loss (BCEL) function imposes a large penalty for classifying any Covid class to Normal class. The experimental results reveal that the proposed InceptionResNetV2 model outperforms the other pre-trained model with training, validation and test accuracy of 99.2%, 98% and 97% respectively.
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Affiliation(s)
| | | | | | - Babita Pathik
- Sagar Institute of Science and Technology, Bhopal, India
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40
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Kaushik B, Koundal D, Goel N, Zaguia A, Belay A, Turabieh H. Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7124199. [PMID: 35800691 PMCID: PMC9253872 DOI: 10.1155/2022/7124199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/24/2022] [Accepted: 04/30/2022] [Indexed: 12/04/2022]
Abstract
Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs.
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Affiliation(s)
- Bhavana Kaushik
- School of Computer Science, University of Petroleum & Energy Studies, Bidholi, Dehradun, India
| | - Deepika Koundal
- School of Computer Science, University of Petroleum & Energy Studies, Bidholi, Dehradun, India
| | - Neelam Goel
- University Institute of Engineering and Technology, Panjab University, Chandigarh, India
| | - Atef Zaguia
- Department of Information Technology, College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Assaye Belay
- Department of Statistics, Mizan-Tepi University, Tepi, Ethiopia
| | - Hamza Turabieh
- Department of Information Technology, College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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41
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Ahmad M, Sadiq S, Eshmawi AA, Alluhaidan AS, Umer M, Ullah S, Nappi M. Industry 4.0 technologies and their applications in fighting COVID-19 pandemic using deep learning techniques. Comput Biol Med 2022; 145:105418. [PMID: 35334315 PMCID: PMC8935962 DOI: 10.1016/j.compbiomed.2022.105418] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 03/16/2022] [Accepted: 03/16/2022] [Indexed: 12/16/2022]
Abstract
The disease known as COVID-19 has turned into a pandemic and spread all over the world. The fourth industrial revolution known as Industry 4.0 includes digitization, the Internet of Things, and artificial intelligence. Industry 4.0 has the potential to fulfil customized requirements during the COVID-19 emergency crises. The development of a prediction framework can help health authorities to react appropriately and rapidly. Clinical imaging like X-rays and computed tomography (CT) can play a significant part in the early diagnosis of COVID-19 patients that will help with appropriate treatment. The X-ray images could help in developing an automated system for the rapid identification of COVID-19 patients. This study makes use of a deep convolutional neural network (CNN) to extract significant features and discriminate X-ray images of infected patients from non-infected ones. Multiple image processing techniques are used to extract a region of interest (ROI) from the entire X-ray image. The ImageDataGenerator class is used to overcome the small dataset size and generate ten thousand augmented images. The performance of the proposed approach has been compared with state-of-the-art VGG16, AlexNet, and InceptionV3 models. Results demonstrate that the proposed CNN model outperforms other baseline models with high accuracy values: 97.68% for two classes, 89.85% for three classes, and 84.76% for four classes. This system allows COVID-19 patients to be processed by an automated screening system with minimal human contact.
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Affiliation(s)
- Muhammad Ahmad
- Department of Information Security, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Saima Sadiq
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Ala’ Abdulmajid Eshmawi
- Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
| | - Ala Saleh Alluhaidan
- Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Muhammad Umer
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan,Corresponding author. Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
| | - Saleem Ullah
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Michele Nappi
- Department of Computer Science, University of Salerno, Fisciano, Italy,Corresponding author
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42
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Kiziloluk S, Sert E. COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer. Med Biol Eng Comput 2022; 60:1595-1612. [PMID: 35396625 PMCID: PMC8993211 DOI: 10.1007/s11517-022-02553-9] [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: 11/01/2021] [Accepted: 03/12/2022] [Indexed: 02/01/2023]
Abstract
Coronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription–polymerase chain reaction (RT-PCR) test is used for the diagnosis of COVID-19 in national healthcare centers. Because the number of PCR test kits is often limited, it is sometimes difficult to diagnose the disease at an early stage. However, X-ray technology is accessible nearly all over the world, and it succeeds in detecting symptoms of COVID-19 more successfully. Another disease which affects people’s lives to a great extent is colorectal cancer. Tissue microarray (TMA) is a technological method which is widely used for its high performance in the analysis of colorectal cancer. Computer-assisted approaches which can classify colorectal cancer in TMA images are also needed. In this respect, the present study proposes a convolutional neural network (CNN) classification approach with optimized parameters using gradient-based optimizer (GBO) algorithm. Thanks to the proposed approach, COVID-19, normal, and viral pneumonia in various chest X-ray images can be classified accurately. Additionally, other types such as epithelial and stromal regions in epidermal growth factor receptor (EFGR) colon in TMAs can also be classified. The proposed approach was called COVID-CCD-Net. AlexNet, DarkNet-19, Inception-v3, MobileNet, ResNet-18, and ShuffleNet architectures were used in COVID-CCD-Net, and the hyperparameters of this architecture was optimized for the proposed approach. Two different medical image classification datasets, namely, COVID-19 and Epistroma, were used in the present study. The experimental findings demonstrated that proposed approach increased the classification performance of the non-optimized CNN architectures significantly and displayed a very high classification performance even in very low value of epoch.
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Affiliation(s)
- Soner Kiziloluk
- Department of Computer Engineering, Malatya Turgut Özal University, Malatya, Turkey
| | - Eser Sert
- Department of Computer Engineering, Malatya Turgut Özal University, Malatya, Turkey
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43
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Chandrasekar KS. Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:5381-5395. [PMID: 35645554 PMCID: PMC9126247 DOI: 10.1007/s11831-022-09768-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
The deadly coronavirus (COVID-19) is one of the dangerous diseases affecting the entire world and is fastly spreading disease. This spread can be reduced by detecting and quarantining the patients at an earlier stage. The most common diagnostic tool for detecting the coronavirus is the Reverse transcription-polymerase chain reaction (RT-PCR) test which is time-consuming and also needs more equipment and manpower. Furthermore, many countries had a deficit of RTPCR kits. This is why it is exceptionally very crucial to develop artificial intelligence (AI) techniques to detect the outbreak of coronavirus. This motivated many researchers to involve deep-learning methods using X-ray images for more decisive analysis. Thus, this paper outlines many papers that used traditional and pre-trained deep learning methods that are newly developed to reduce the spread of COVID-19 disease. Specifically, advanced deep learning methods play a critical role in extracting the features from the chest X-ray images. These features are then used to classify whether the patient is affected with coronavirus or not. Besides, this paper shows that deep learning techniques have probable applications in the medical field.
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44
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Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN COMPUTER SCIENCE 2022; 3:286. [PMID: 35578678 PMCID: PMC9096341 DOI: 10.1007/s42979-022-01184-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 04/30/2022] [Indexed: 12/12/2022]
Abstract
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
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Affiliation(s)
- Yassine Meraihi
- LIST Laboratory, University of M'Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
| | - Asma Benmessaoud Gabis
- Ecole nationale Supérieure d'Informatique, Laboratoire des Méthodes de Conception des Systèmes, BP 68 M, 16309 Oued-Smar, Alger Algeria
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia.,Yonsei Frontier Lab, Yonsei University, Seoul, Korea
| | - Amar Ramdane-Cherif
- LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10-12 Avenue of Europe, 78140 Velizy, France
| | - Fawaz E Alsaadi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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45
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Wang H, Jia S, Li Z, Duan Y, Tao G, Zhao Z. A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic. Front Genet 2022; 13:845305. [PMID: 35559010 PMCID: PMC9086537 DOI: 10.3389/fgene.2022.845305] [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: 12/29/2021] [Accepted: 03/30/2022] [Indexed: 11/13/2022] Open
Abstract
The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19.
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Affiliation(s)
- Haishuai Wang
- College of Computer Science, Zhejiang University, Hangzhou, China
| | - Shangru Jia
- Department of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Zhao Li
- Alibaba-ZJU Joint Research Institute of Frontier Technologies, Zhejiang University, Hangzhou, China
| | - Yucong Duan
- College of Computer Science and Technology, Hainan University, Haikou, China
| | - Guangyu Tao
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Ziping Zhao
- Department of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
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46
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Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images. Diagnostics (Basel) 2022; 12:diagnostics12040915. [PMID: 35453963 PMCID: PMC9025806 DOI: 10.3390/diagnostics12040915] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/28/2022] [Accepted: 04/04/2022] [Indexed: 12/04/2022] Open
Abstract
Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.
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47
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Narayan Das N, Kumar N, Kaur M, Kumar V, Singh D. Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays. Ing Rech Biomed 2022; 43:114-119. [PMID: 32837679 PMCID: PMC7333623 DOI: 10.1016/j.irbm.2020.07.001] [Citation(s) in RCA: 96] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 06/24/2020] [Accepted: 07/01/2020] [Indexed: 12/28/2022]
Abstract
The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient. Due to less sensitivity of RT-PCR, it provides high false-negative results. To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19. In this paper, chest X-rays is preferred over CT scan. The reason behind this is that X-rays machines are available in most of the hospitals. X-rays machines are cheaper than the CT scan machine. Besides this, X-rays has low ionizing radiations than CT scan. COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays. For this, radiologists are required to analyze these signatures. However, it is a time-consuming and error-prone task. Hence, there is a need to automate the analysis of chest X-rays. The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time. These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets. However, these approaches applied to chest X-rays are very limited. Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model. Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models.
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Affiliation(s)
- N Narayan Das
- Department of Information Technology, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
| | - N Kumar
- Department Of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi, 110058, India
| | - M Kaur
- Department of Computer and Communication Engineering, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
| | - V Kumar
- Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, 177005, India
| | - D Singh
- Department of Computer Science and Engineering, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
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Kumar R, Arora R, Bansal V, Sahayasheela VJ, Buckchash H, Imran J, Narayanan N, Pandian GN, Raman B. Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:27631-27655. [PMID: 35368858 PMCID: PMC8958819 DOI: 10.1007/s11042-022-12500-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/18/2021] [Accepted: 01/25/2022] [Indexed: 06/12/2023]
Abstract
COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.
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Affiliation(s)
- Rahul Kumar
- Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India
- Department of Computer Engineering & Applications, GLA University, Mathura, Uttar Pradesh India
| | - Ridhi Arora
- Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Vipul Bansal
- Department of Mechanical & Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Vinodh J Sahayasheela
- Institute of Integrated Cell Material Sciences (WPI-iCeMS), Kyoto University of Advanced Study, Kyoto, Japan
| | - Himanshu Buckchash
- Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Javed Imran
- School of Computer Science, University of Petroleum & Energy Studies (UPES), Dehradun, India
| | - Narayanan Narayanan
- Centre for Research and Graduate Studies, University of CyberJaya, Sepang, Malaysia
| | - Ganesh N Pandian
- Institute of Integrated Cell Material Sciences (WPI-iCeMS), Kyoto University of Advanced Study, Kyoto, Japan
| | - Balasubramanian Raman
- Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India
- Centre for Research and Graduate Studies, University of CyberJaya, Sepang, Malaysia
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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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Asif S, Zhao M, Tang F, Zhu Y. A deep learning-based framework for detecting COVID-19 patients using chest X-rays. MULTIMEDIA SYSTEMS 2022; 28:1495-1513. [PMID: 35341212 PMCID: PMC8939400 DOI: 10.1007/s00530-022-00917-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 03/09/2022] [Indexed: 06/02/2023]
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused outbreaks of new coronavirus disease (COVID-19) around the world. Rapid and accurate detection of COVID-19 coronavirus is an important step in limiting the spread of the COVID-19 epidemic. To solve this problem, radiography techniques (such as chest X-rays and computed tomography (CT)) can play an important role in the early prediction of COVID-19 patients, which will help to treat patients in a timely manner. We aimed to quickly develop a highly efficient lightweight CNN architecture for detecting COVID-19-infected patients. The purpose of this paper is to propose a robust deep learning-based system for reliably detecting COVID-19 from chest X-ray images. First, we evaluate the performance of various pre-trained deep learning models (InceptionV3, Xception, MobileNetV2, NasNet and DenseNet201) recently proposed for medical image classification. Second, a lightweight shallow convolutional neural network (CNN) architecture is proposed for classifying X-ray images of a patient with a low false-negative rate. The data set used in this work contains 2,541 chest X-rays from two different public databases, which have confirmed COVID-19 positive and healthy cases. The performance of the proposed model is compared with the performance of pre-trained deep learning models. The results show that the proposed shallow CNN provides a maximum accuracy of 99.68% and more importantly sensitivity, specificity and AUC of 99.66%, 99.70% and 99.98%. The proposed model has fewer parameters and low complexity compared to other deep learning models. The experimental results of our proposed method show that it is superior to the existing state-of-the-art methods. We believe that this model can help healthcare professionals to treat COVID-19 patients through improved and faster patient screening.
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Affiliation(s)
- Sohaib Asif
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Ming Zhao
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Fengxiao Tang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Yusen Zhu
- School of Mathematics, Hunan University, Changsha, China
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