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Antar S, Abd El-Sattar HKH, Abdel-Rahman MH, F M Ghaleb F. COVID-19 infection segmentation using hybrid deep learning and image processing techniques. Sci Rep 2023; 13:22737. [PMID: 38123587 PMCID: PMC10733411 DOI: 10.1038/s41598-023-49337-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) epidemic has become a worldwide problem that continues to affect people's lives daily, and the early diagnosis of COVID-19 has a critical importance on the treatment of infected patients for medical and healthcare organizations. To detect COVID-19 infections, medical imaging techniques, including computed tomography (CT) scan images and X-ray images, are considered some of the helpful medical tests that healthcare providers carry out. However, in addition to the difficulty of segmenting contaminated areas from CT scan images, these approaches also offer limited accuracy for identifying the virus. Accordingly, this paper addresses the effectiveness of using deep learning (DL) and image processing techniques, which serve to expand the dataset without the need for any augmentation strategies, and it also presents a novel approach for detecting COVID-19 virus infections in lung images, particularly the infection prediction issue. In our proposed method, to reveal the infection, the input images are first preprocessed using a threshold then resized to 128 × 128. After that, a density heat map tool is used for coloring the resized lung images. The three channels (red, green, and blue) are then separated from the colored image and are further preprocessed through image inverse and histogram equalization, and are subsequently fed, in independent directions, into three separate U-Nets with the same architecture for segmentation. Finally, the segmentation results are combined and run through a convolution layer one by one to get the detection. Several evaluation metrics using the CT scan dataset were used to measure the performance of the proposed approach in comparison with other state-of-the-art techniques in terms of accuracy, sensitivity, precision, and the dice coefficient. The experimental results of the proposed approach reached 99.71%, 0.83, 0.87, and 0.85, respectively. These results show that coloring the CT scan images dataset and then dividing each image into its RGB image channels can enhance the COVID-19 detection, and it also increases the U-Net power in the segmentation when merging the channel segmentation results. In comparison to other existing segmentation techniques employing bigger 512 × 512 images, this study is one of the few that can rapidly and correctly detect the COVID-19 virus with high accuracy on smaller 128 × 128 images using the metrics of accuracy, sensitivity, precision, and dice coefficient.
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Affiliation(s)
- Samar Antar
- Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt
| | | | - Mohammad H Abdel-Rahman
- Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt
| | - Fayed F M Ghaleb
- Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt
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2
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Al-Sheikh MH, Al Dandan O, Al-Shamayleh AS, Jalab HA, Ibrahim RW. Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images. Sci Rep 2023; 13:19373. [PMID: 37938631 PMCID: PMC10632494 DOI: 10.1038/s41598-023-46147-3] [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: 06/15/2023] [Accepted: 10/27/2023] [Indexed: 11/09/2023] Open
Abstract
Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest X-Ray and CT scans are utilized to check the disease's spread throughout the lungs. This study proposes an automated system for the detection multi lung diseases in X-Ray and CT scans. A customized convolutional neural network (CNN) and two pre-trained deep learning models with a new image enhancement model are proposed for image classification. The proposed lung disease detection comprises two main steps: pre-processing, and deep learning classification. The new image enhancement algorithm is developed in the pre-processing step using k-symbol Lerch transcendent functions model which enhancement images based on image pixel probability. While, in the classification step, the customized CNN architecture and two pre-trained CNN models Alex Net, and VGG16Net are developed. The proposed approach was tested on publicly available image datasets (CT, and X-Ray image dataset), and the results showed classification accuracy, sensitivity, and specificity of 98.60%, 98.40%, and 98.50% for the X-Ray image dataset, respectively, and 98.80%, 98.50%, 98.40% for the CT scans dataset, respectively. Overall, the obtained results highlight the advantages of the image enhancement model as a first step in processing.
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Affiliation(s)
- Mona Hmoud Al-Sheikh
- Physiology Department, College of Medicine, Imam Abdulrahman Bin Faisal University, 34212, Dammam, Saudi Arabia
| | - Omran Al Dandan
- Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, 34212, Dammam, Saudi Arabia
| | - Ahmad Sami Al-Shamayleh
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Al-Salt, Amman, 19328, Jordan
| | - Hamid A Jalab
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nile Street, 64001, Thi-Qar, Iraq.
| | - Rabha W Ibrahim
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nile Street, 64001, Thi-Qar, Iraq
- Department of Mathematics, Mathematics Research Center, Near East University, Near East Boulevard, PC: 99138, Nicosia/Mersin 10, Turkey
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, 1102 2801, Lebanon
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3
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Bakasa W, Viriri S. Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting. Front Artif Intell 2023; 6:1232640. [PMID: 37876961 PMCID: PMC10591225 DOI: 10.3389/frai.2023.1232640] [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: 05/31/2023] [Accepted: 09/04/2023] [Indexed: 10/26/2023] Open
Abstract
Ensemble learning aims to improve prediction performance by combining several models or forecasts. However, how much and which ensemble learning techniques are useful in deep learning-based pipelines for pancreas computed tomography (CT) image classification is a challenge. Ensemble approaches are the most advanced solution to many machine learning problems. These techniques entail training multiple models and combining their predictions to improve the predictive performance of a single model. This article introduces the idea of Stacked Ensemble Deep Learning (SEDL), a pipeline for classifying pancreas CT medical images. The weak learners are Inception V3, VGG16, and ResNet34, and we employed a stacking ensemble. By combining the first-level predictions, an input train set for XGBoost, the ensemble model at the second level of prediction, is created. Extreme Gradient Boosting (XGBoost), employed as a strong learner, will make the final classification. Our findings showed that SEDL performed better, with a 98.8% ensemble accuracy, after some adjustments to the hyperparameters. The Cancer Imaging Archive (TCIA) public access dataset consists of 80 pancreas CT scans with a resolution of 512 * 512 pixels, from 53 male and 27 female subjects. A sample of two hundred and twenty-two images was used for training and testing data. We concluded that implementing the SEDL technique is an effective way to strengthen the robustness and increase the performance of the pipeline for classifying pancreas CT medical images. Interestingly, grouping like-minded or talented learners does not make a difference.
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Affiliation(s)
| | - Serestina Viriri
- School of Mathematics Statistics & Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban, South Africa
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4
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Guha SK, Sadhukhan S, Niyogi S. COVID-19 cluster identification and support vector machine classifier model construction using global healthcare and socio-economic features. Epidemiol Infect 2023; 151:e159. [PMID: 37646158 PMCID: PMC10600736 DOI: 10.1017/s0950268823001383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 08/22/2023] [Indexed: 09/01/2023] Open
Abstract
Coronaviruses of the human variety have been the culprit of global epidemics of varying levels of lethality, including COVID-19, which has impacted more than 200 countries and resulted in 5.7 million fatalities as of May 2022. Effective clinical management necessitates the allocation of sufficient resources and the employment of appropriately skilled personnel. The elderly population and individuals with diabetes are at increased risk of more severe manifestations of COVID-19. Countries with a higher gross domestic product (GDP) typically exhibit superior health outcomes and reduced mortality rates. Here, we suggest a predictive model for the density of medical doctors and nursing personnel for 134 countries using a support vector machine (SVM). The model was trained in 107 countries and tested in 27, with promising results shown by the kappa statistics and ROC analysis. The SVM model used for predictions showed promising results with a high level of agreement between actual and predicted cluster values.
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Affiliation(s)
- Soumya Kanti Guha
- Department of Computer Application, Dinabandhu Andrews Institute of Technology and Management, Kolkata, India
| | - Sandip Sadhukhan
- Department of Computer Application, Dinabandhu Andrews Institute of Technology and Management, Kolkata, India
| | - Sougata Niyogi
- Department of Medical Lab Technology, Dinabandhu Andrews Institute of Technology and Management, Kolkata, India
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5
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Zaeri N. Artificial intelligence and machine learning responses to COVID-19 related inquiries. J Med Eng Technol 2023; 47:301-320. [PMID: 38625639 DOI: 10.1080/03091902.2024.2321846] [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: 11/14/2021] [Accepted: 02/18/2024] [Indexed: 04/17/2024]
Abstract
Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.
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Affiliation(s)
- Naser Zaeri
- Faculty of Computer Studies, Arab Open University, Kuwait
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6
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Liu J, Zhang T, Kang Y, Wang Y, Zhang Y, Hu D, Chen Y. Deep residual constrained reconstruction via learned convolutional sparse coding for low-dose CT imaging. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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7
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Mehrdad S, Shamout FE, Wang Y, Atashzar SF. Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs. Sci Rep 2023; 13:9968. [PMID: 37339986 DOI: 10.1038/s41598-023-37013-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 06/14/2023] [Indexed: 06/22/2023] Open
Abstract
Unrecognized deterioration of COVID-19 patients can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images or comprehensive laboratory tests. This is infeasible for telehealth solutions and highlights a gap in deterioration prediction models based on minimal data, which can be recorded at a large scale in any clinic, nursing home, or even at the patient's home. In this study, we develop and compare two prognostic models that predict if a patient will experience deterioration in the forthcoming 3 to 24 h. The models sequentially process routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c) temperature. These models are also provided with basic patient information, including sex, age, vaccination status, vaccination date, and status of obesity, hypertension, or diabetes. The difference between the two models is the way that the temporal dynamics of the vital signs are processed. Model #1 utilizes a temporally-dilated version of the Long-Short Term Memory model (LSTM) for temporal processes, and Model #2 utilizes a residual temporal convolutional network (TCN) for this purpose. We train and evaluate the models using data collected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA. The convolution-based model outperforms the LSTM based model, achieving a high AUROC of 0.8844-0.9336 for 3 to 24 h deterioration prediction on a held-out test set. We also conduct occlusion experiments to evaluate the importance of each input feature, which reveals the significance of continuously monitoring the variation of the vital signs. Our results show the prospect for accurate deterioration forecast using a minimum feature set that can be relatively easily obtained using wearable devices and self-reported patient information.
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Affiliation(s)
- Sarmad Mehrdad
- Department of Electrical and Computer Engineering, New York University (NYU), New York, USA
| | - Farah E Shamout
- Department of Biomedical Engineering, New York University (NYU), New York, USA
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE
- Computer Science and Engineering, New York University (NYU), New York, USA
| | - Yao Wang
- Department of Electrical and Computer Engineering, New York University (NYU), New York, USA
- Department of Biomedical Engineering, New York University (NYU), New York, USA
| | - S Farokh Atashzar
- Department of Electrical and Computer Engineering, New York University (NYU), New York, USA.
- Department of Biomedical Engineering, New York University (NYU), New York, USA.
- Department of Mechanical and Aerospace Engineering, New York University (NYU), New York, USA.
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Hsu CC, Ameri R, Lin CW, He JS, Biyari M, Yarahmadi A, Band SS, Lin TK, Fan WL. A robust approach for endotracheal tube localization in chest radiographs. Front Artif Intell 2023; 6:1181812. [PMID: 37251274 PMCID: PMC10219610 DOI: 10.3389/frai.2023.1181812] [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: 03/12/2023] [Accepted: 04/13/2023] [Indexed: 05/31/2023] Open
Abstract
Precise detection and localization of the Endotracheal tube (ETT) is essential for patients receiving chest radiographs. A robust deep learning model based on U-Net++ architecture is presented for accurate segmentation and localization of the ETT. Different types of loss functions related to distribution and region-based loss functions are evaluated in this paper. Then, various integrations of distribution and region-based loss functions (compound loss function) have been applied to obtain the best intersection over union (IOU) for ETT segmentation. The main purpose of the presented study is to maximize IOU for ETT segmentation, and also minimize the error range that needs to be considered during calculation of distance between the real and predicted ETT by obtaining the best integration of the distribution and region loss functions (compound loss function) for training the U-Net++ model. We analyzed the performance of our model using chest radiograph from the Dalin Tzu Chi Hospital in Taiwan. The results of applying the integration of distribution-based and region-based loss functions on the Dalin Tzu Chi Hospital dataset show enhanced segmentation performance compared to other single loss functions. Moreover, according to the obtained results, the combination of Matthews Correlation Coefficient (MCC) and Tversky loss functions, which is a hybrid loss function, has shown the best performance on ETT segmentation based on its ground truth with an IOU value of 0.8683.
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Affiliation(s)
- Chung-Chian Hsu
- Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Rasoul Ameri
- Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Chih-Wen Lin
- Buddhist Dalin Tzu Chi Hospital, Chiayi, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | | | - Meghdad Biyari
- Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Atefeh Yarahmadi
- Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Shahab S. Band
- Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan
- International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Tin-Kwang Lin
- Buddhist Dalin Tzu Chi Hospital, Chiayi, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Wen-Lin Fan
- Buddhist Dalin Tzu Chi Hospital, Chiayi, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
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Yang S, Xing Z, Wang H, Gao X, Dong X, Yao Y, Zhang R, Zhang X, Li S, Zhao Y, Liu Z. Classification and localization of maize leaf spot disease based on weakly supervised learning. FRONTIERS IN PLANT SCIENCE 2023; 14:1128399. [PMID: 37223797 PMCID: PMC10201986 DOI: 10.3389/fpls.2023.1128399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/10/2023] [Indexed: 05/25/2023]
Abstract
Precisely discerning disease types and vulnerable areas is crucial in implementing effective monitoring of crop production. This forms the basis for generating targeted plant protection recommendations and automatic, precise applications. In this study, we constructed a dataset comprising six types of field maize leaf images and developed a framework for classifying and localizing maize leaf diseases. Our approach involved integrating lightweight convolutional neural networks with interpretable AI algorithms, which resulted in high classification accuracy and fast detection speeds. To evaluate the performance of our framework, we tested the mean Intersection over Union (mIoU) of localized disease spot coverage and actual disease spot coverage when relying solely on image-level annotations. The results showed that our framework achieved a mIoU of up to 55.302%, indicating the feasibility of using weakly supervised semantic segmentation based on class activation mapping techniques for identifying disease spots in crop disease detection. This approach, which combines deep learning models with visualization techniques, improves the interpretability of the deep learning models and achieves successful localization of infected areas of maize leaves through weakly supervised learning. The framework allows for smart monitoring of crop diseases and plant protection operations using mobile phones, smart farm machines, and other devices. Furthermore, it offers a reference for deep learning research on crop diseases.
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Affiliation(s)
- Shuai Yang
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Ziyao Xing
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Hengbin Wang
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xiang Gao
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xinrui Dong
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yu Yao
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Runda Zhang
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xiaodong Zhang
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Shaoming Li
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yuanyuan Zhao
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhe Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
- Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing, China
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Manav M, Goyal M, Kumar A. Role of Optimal Features Selection with Machine Learning Algorithms for Chest X-ray Image Analysis. J Med Phys 2023; 48:195-203. [PMID: 37576090 PMCID: PMC10419742 DOI: 10.4103/jmp.jmp_104_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 04/26/2023] [Accepted: 05/06/2023] [Indexed: 08/15/2023] Open
Abstract
Introduction The objective of the present study is to classify chest X-ray (CXR) images into COVID-positive and normal categories with the optimal number of features extracted from the images. The successful optimal feature selection algorithm that can represent images and the classification algorithm with good classification ability has been determined as the result of experiments. Materials and Methods This study presented a framework for the automatic detection of COVID-19 from the CXR images. To enhance small details, textures, and contrast of the images, contrast limited adaptive histogram equalization was used. Features were extracted from the first-order statistics, Gray-Level Co-occurrence Matrix, Gray-Level Run Length Matrix, local binary pattern, Law's Texture Energy Measures, Discrete Wavelet Transform, and Zernikes' Moments using an image feature extraction tool "pyFeats. For the feature selection, three nature-inspired optimization algorithms, Grey Wolf Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm, were used. For classification, Random Forest classifier, K-Nearest Neighbour classifier, support vector machine (SVM) classifier, and light gradient boosting model classifier were used. Results and Discussion For all the feature selection methods, the SVM classifier gives the most accurate and precise result compared to other classification models. Furthermore, in feature selection methods, PSO gives the best result as compared to other methods for feature selection. Using the combination of the SVM classifier with the PSO method, it was observed that the accuracy, precision, recall, and F1-score were 100%. Conclusion The result of the study indicates that with optimal features with the best choice of the classifier algorithm, the most accurate computer-aided diagnosis of CXR can be achieved. The approach presented in this study with optimal features may be utilized as a complementary tool to assist the radiologist in the early diagnosis of disease and making a more accurate decision.
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Affiliation(s)
- Mohini Manav
- Department of Physics, GLA University, Mathura, Uttar Pradesh, India
- Department of Radiotherapy, S N Medical College, Agra, Uttar Pradesh, India
| | - Monika Goyal
- Department of Physics, GLA University, Mathura, Uttar Pradesh, India
| | - Anuj Kumar
- Department of Radiotherapy, S N Medical College, Agra, Uttar Pradesh, India
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Hammad MS, Ghoneim VF, Mabrouk MS, Al-Atabany WI. A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques. Sci Rep 2023; 13:4003. [PMID: 36899035 PMCID: PMC9999081 DOI: 10.1038/s41598-023-30941-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 03/03/2023] [Indexed: 03/12/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.
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Affiliation(s)
- Muhammed S Hammad
- Biomedical Engineering Department, Helwan University, Helwan, Egypt.
| | - Vidan F Ghoneim
- Biomedical Engineering Department, Helwan University, Helwan, Egypt
| | - Mai S Mabrouk
- Biomedical Engineering Department, Misr University for Science and Technology (MUST), 6th of October City, Egypt
| | - Walid I Al-Atabany
- Biomedical Engineering Department, Helwan University, Helwan, Egypt.,Center for Informatics Science, Nile University, Sheikh Zayed City, Egypt
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12
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Calibrated bagging deep learning for image semantic segmentation: A case study on COVID-19 chest X-ray image. PLoS One 2022; 17:e0276250. [DOI: 10.1371/journal.pone.0276250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 10/03/2022] [Indexed: 11/17/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainty.
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13
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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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14
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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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15
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Hamza A, Attique Khan M, Wang SH, Alqahtani A, Alsubai S, Binbusayyis A, Hussein HS, Martinetz TM, Alshazly H. COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization. Front Public Health 2022; 10:948205. [PMID: 36111186 PMCID: PMC9468600 DOI: 10.3389/fpubh.2022.948205] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/01/2022] [Indexed: 01/21/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.
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Affiliation(s)
- Ameer Hamza
- Department of Computer Science, HITEC University, Taxila, Pakistan
| | - Muhammad Attique Khan
- Department of Computer Science, HITEC University, Taxila, Pakistan,*Correspondence: Muhammad Attique Khan
| | - Shui-Hua Wang
- Department of Mathematics, University of Leicester, Leicester, United Kingdom
| | - Abdullah Alqahtani
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Shtwai Alsubai
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Adel Binbusayyis
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Hany S. Hussein
- Department of Electrical Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia,Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt
| | | | - Hammam Alshazly
- Faculty of Computers and Information, South Valley University, Qena, Egypt
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16
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Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1307944. [PMID: 35996653 PMCID: PMC9392599 DOI: 10.1155/2022/1307944] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 03/16/2022] [Accepted: 07/19/2022] [Indexed: 02/07/2023]
Abstract
Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.
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17
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FocusedDropout for Convolutional Neural Network. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In a convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially. Except for randomly discarding regions or channels, many approaches try to overcome this defect by dropping influential units. In this paper, we propose a non-random dropout method named FocusedDropout, aiming to make the network focus more on the target. In FocusedDropout, we use a simple but effective method to search for the target-related features, retain these features and discard others, which is contrary to the existing methods. We find that this novel method can improve network performance by making the network more target focused. Additionally, increasing the weight decay while using FocusedDropout can avoid overfitting and increase accuracy. Experimental results show that with a slight cost, 10% of batches employing FocusedDropout, can produce a nice performance boost over the baselines on multiple datasets of classification, including CIFAR10, CIFAR100 and Tiny ImageNet, and has a good versatility for different CNN models.
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18
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Band SS, Ardabili S, Yarahmadi A, Pahlevanzadeh B, Kiani AK, Beheshti A, Alinejad-Rokny H, Dehzangi I, Chang A, Mosavi A, Moslehpour M. A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis. Front Public Health 2022; 10:869238. [PMID: 35812486 PMCID: PMC9260273 DOI: 10.3389/fpubh.2022.869238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.
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Affiliation(s)
- Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Sina Ardabili
- Department of Informatics, J. Selye University, Komárom, Slovakia
| | - Atefeh Yarahmadi
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Bahareh Pahlevanzadeh
- Department of Design and System Operations, Regional Information Center for Science and Technology (R.I.C.E.S.T.), Shiraz, Iran
| | - Adiqa Kausar Kiani
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Amin Beheshti
- Department of Computing, Macquarie University, Sydney, NSW, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, U.N.S.W. Sydney, Sydney, NSW, Australia
- U.N.S.W. Data Science Hub, The University of New South Wales (U.N.S.W. Sydney), Sydney, NSW, Australia
- Health Data Analytics Program, AI-enabled Processes (A.I.P.) Research Centre, Macquarie University, Sydney, NSW, Australia
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, United States
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Arthur Chang
- Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Massoud Moslehpour
- Department of Business Administration, College of Management, Asia University, Taichung, Taiwan
- Department of Management, California State University, San Bernardino, CA, United States
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19
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Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B. Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106731. [PMID: 35286874 PMCID: PMC8897838 DOI: 10.1016/j.cmpb.2022.106731] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/28/2022] [Accepted: 03/03/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.
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Affiliation(s)
- Haseeb Hassan
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China
| | - Zhaoyu Ren
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Chengmin Zhou
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Yi Pan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Jian Zhao
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
| | - Bingding Huang
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
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20
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Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:7377502. [PMID: 35280708 PMCID: PMC8896964 DOI: 10.1155/2022/7377502] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/24/2022] [Indexed: 12/17/2022]
Abstract
Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.
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21
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Tiwari S, Jain A. A lightweight capsule network architecture for detection of COVID-19 from lung CT scans. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:419-434. [PMID: 35465213 PMCID: PMC9015631 DOI: 10.1002/ima.22706] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 11/22/2021] [Accepted: 01/04/2022] [Indexed: 05/28/2023]
Abstract
COVID-19, a novel coronavirus, has spread quickly and produced a worldwide respiratory ailment outbreak. There is a need for large-scale screening to prevent the spreading of the disease. When compared with the reverse transcription polymerase chain reaction (RT-PCR) test, computed tomography (CT) is far more consistent, concrete, and precise in detecting COVID-19 patients through clinical diagnosis. An architecture based on deep learning has been proposed by integrating a capsule network with different variants of convolution neural networks. DenseNet, ResNet, VGGNet, and MobileNet are utilized with CapsNet to detect COVID-19 cases using lung computed tomography scans. It has found that all the four models are providing adequate accuracy, among which the VGGCapsNet, DenseCapsNet, and MobileCapsNet models have gained the highest accuracy of 99%. An Android-based app can be deployed using MobileCapsNet model to detect COVID-19 as it is a lightweight model and best suited for handheld devices like a mobile.
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Affiliation(s)
- Shamik Tiwari
- School of Computer ScienceUniversity of Petroleum and Energy StudiesDehradunUttarakhandIndia
| | - Anurag Jain
- School of Computer ScienceUniversity of Petroleum and Energy StudiesDehradunUttarakhandIndia
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22
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Chahar S, Roy PK. COVID-19: A Comprehensive Review of Learning Models. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:1915-1940. [PMID: 34566404 PMCID: PMC8449694 DOI: 10.1007/s11831-021-09641-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/31/2021] [Indexed: 05/17/2023]
Abstract
Coronavirus disease is communicable and inhibits the infected person's immune system. It belongs to the Coronaviridae family and has affected 213 nations and territories so far. Many kinds of studies are being carried out to filter advice and provide oversight to monitor this outbreak. A comparative and brief review was carried out in this paper on research concerning the early identification of symptoms, estimation of the end of the pandemic, and examination of user-generated conversations. Chest X-ray images, abdominal computed tomography scan, tweets shared on social media are several of the datasets used by researchers. Using machine learning and deep learning methods such as K-means clustering, Random Forest, Convolutional Neural Network, Long Short-Term Memory, Auto-Encoder, and Regression approaches, the above-mentioned datasets are processed. The studies on COVID-19 with machine learning and deep learning models with their results and limitations are outlined in this article. The challenges with open future research directions are discussed at the end.
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Affiliation(s)
- Shivam Chahar
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, TN India
| | - Pradeep Kumar Roy
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Surat, Gujarat India
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23
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El-Shafai W, A. Mahmoud A, M. El-Rabaie ES, E. Taha T, F. Zahran O, S. El-Fishawy A, Abd-Elnaby M, E. Abd El-Samie F. Efficient Deep CNN Model for COVID-19 Classification. COMPUTERS, MATERIALS & CONTINUA 2022; 70:4373-4391. [DOI: 10.32604/cmc.2022.019354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 06/18/2021] [Indexed: 09/01/2023]
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24
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Niu S, Liu M, Liu Y, Wang J, Song H. Distant Domain Transfer Learning for Medical Imaging. IEEE J Biomed Health Inform 2021; 25:3784-3793. [PMID: 33449887 PMCID: PMC8545174 DOI: 10.1109/jbhi.2021.3051470] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/07/2021] [Accepted: 01/10/2021] [Indexed: 11/09/2022]
Abstract
Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method COVID-19 diagnosis with lung Computed Tomography (CT) images. However, well-labeled training data sets cannot be easily accessed due to the disease's novelty and privacy policies. The proposed method has two components: reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnosis using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.
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Affiliation(s)
- Shuteng Niu
- Security and Optimization for Networked Globe Laboratory (SONG Lab), Department of Computer Science and Electrical EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachFL32114USA
| | - Meryl Liu
- Spruce Creek High SchoolPort OrangeFL32128USA
| | - Yongxin Liu
- Security and Optimization for Networked Globe Laboratory (SONG Lab), Department of Computer Science and Electrical EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachFL32114USA
| | - Jian Wang
- Security and Optimization for Networked Globe Laboratory (SONG Lab), Department of Computer Science and Electrical EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachFL32114USA
| | - Houbing Song
- Security and Optimization for Networked Globe Laboratory (SONG Lab), Department of Computer Science and Electrical EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachFL32114USA
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25
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Pathan S, Siddalingaswamy PC, Kumar P, Pai M M M, Ali T, Acharya UR. Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images. Comput Biol Med 2021; 137:104835. [PMID: 34508976 PMCID: PMC8418990 DOI: 10.1016/j.compbiomed.2021.104835] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 01/20/2023]
Abstract
The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application.
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Affiliation(s)
- Sameena Pathan
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
| | - P C Siddalingaswamy
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
| | - Preetham Kumar
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
| | - Manohara Pai M M
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
| | - Tanweer Ali
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
| | - U Rajendra Acharya
- Dept. of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.
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26
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Narin A. Accurate detection of COVID-19 using deep features based on X-Ray images and feature selection methods. Comput Biol Med 2021; 137:104771. [PMID: 34450381 PMCID: PMC8373589 DOI: 10.1016/j.compbiomed.2021.104771] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 08/11/2021] [Accepted: 08/14/2021] [Indexed: 01/19/2023]
Abstract
COVID-19 is a severe epidemic affecting the whole world. This epidemic, which has a high mortality rate, affects the health systems and the economies of countries significantly. Therefore, ending the epidemic is one of the most important priorities of all states. For this, automatic diagnosis and detection systems are very important to control the epidemic. In addition to the recommendation of the “reverse transcription-polymerase chain reaction (RT-PCR)” test, additional diagnosis and detection systems are required. Hence, based on the fact that the COVID-19 virus attacks the lungs, automatic diagnosis and detection systems developed using X-ray and CT images come to the fore. In this study, a high-performance detection system was implemented with three different CNN (ResNet50, ResNet101, InceptionResNetV2) models and X-ray images of three different classes (COVID-19, Normal, Pneumonia). The particle swarm optimization (PSO) algorithm and ant colony algorithm (ACO) was applied among the feature selection methods, and their performances were compared. The results were obtained using support vector machines (SVM) and a k-nearest neighbor (k-NN) classifier using the 10-fold cross-validation method. The highest overall accuracy performance was 99.83% with the SVM algorithm without feature selection. The highest performance was achieved after the feature selection process with the SVM + PSO method as 99.86%. As a result, higher performance with less computational load has been achieved by realizing the feature selection. Based on the high results obtained, it is thought that this study will benefit radiologists as a decision support system.
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Affiliation(s)
- Ali Narin
- Zonguldak Bulent Ecevit University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Zonguldak, Turkey.
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Ghaderzadeh M, Aria M, Asadi F. X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9942873. [PMID: 34458373 PMCID: PMC8390162 DOI: 10.1155/2021/9942873] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/16/2021] [Accepted: 08/04/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE Due to the excessive use of raw materials in diagnostic tools and equipment during the COVID-19 pandemic, there is a dire need for cheaper and more effective methods in the healthcare system. With the development of artificial intelligence (AI) methods in medical sciences as low-cost and safer diagnostic methods, researchers have turned their attention to the use of imaging tools with AI that have fewer complications for patients and reduce the consumption of healthcare resources. Despite its limitations, X-ray is suggested as the first-line diagnostic modality for detecting and screening COVID-19 cases. METHOD This systematic review assessed the current state of AI applications and the performance of algorithms in X-ray image analysis. The search strategy yielded 322 results from four databases and google scholar, 60 of which met the inclusion criteria. The performance statistics included the area under the receiver operating characteristics (AUC) curve, accuracy, sensitivity, and specificity. RESULT The average sensitivity and specificity of CXR equipped with AI algorithms for COVID-19 diagnosis were >96% (83%-100%) and 92% (80%-100%), respectively. For common X-ray methods in COVID-19 detection, these values were 0.56 (95% CI 0.51-0.60) and 0.60 (95% CI 0.54-0.65), respectively. AI has substantially improved the diagnostic performance of X-rays in COVID-19. CONCLUSION X-rays equipped with AI can serve as a tool to screen the cases requiring CT scans. The use of this tool does not waste time or impose extra costs, has minimal complications, and can thus decrease or remove unnecessary CT slices and other healthcare resources.
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Affiliation(s)
- Mustafa Ghaderzadeh
- Student Research Committee, Department and Faculty of Health Information Technology and Ma School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrad Aria
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Alshazly H, Linse C, Abdalla M, Barth E, Martinetz T. COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans. PeerJ Comput Sci 2021; 7:e655. [PMID: 34401477 PMCID: PMC8330434 DOI: 10.7717/peerj-cs.655] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/09/2021] [Indexed: 05/10/2023]
Abstract
In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architectures usually suffer from the lack of pretrained weights, our proposed models can be partly initialized with larger baseline models like ResNet50 and DenseNet121, which is attractive because of the abundance of public repositories. The architectures are utilized in a first experimental study on the SARS-CoV-2 CT-scan dataset, which contains 4173 CT images for 210 subjects structured in a subject-wise manner into three different classes. The models differentiate between COVID-19, non-COVID-19 viral pneumonia, and healthy samples. We also investigate their performance under three binary classification scenarios where we distinguish COVID-19 from healthy, COVID-19 from non-COVID-19 viral pneumonia, and non-COVID-19 from healthy, respectively. Our proposed models achieve up to 93.87% accuracy, 99.13% precision, 92.49% sensitivity, 97.73% specificity, 95.70% F1-score, and 96.80% AUC score for binary classification, and up to 83.89% accuracy, 80.36% precision, 82.04% sensitivity, 92.07% specificity, 81.05% F1-score, and 94.20% AUC score for the three-class classification tasks. We also validated our models on the COVID19-CT dataset to differentiate COVID-19 and other non-COVID-19 viral infections, and our CovidDenseNet model achieved the best performance with 81.77% accuracy, 79.05% precision, 84.69% sensitivity, 79.05% specificity, 81.77% F1-score, and 87.50% AUC score. The experimental results reveal the effectiveness of the proposed networks in automated COVID-19 detection where they outperform standard models on the considered datasets while being more efficient.
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Affiliation(s)
- Hammam Alshazly
- Institut für Neuro- und Bioinformatik, University of Lübeck, Lübeck, Germany
- Faculty of Computers and Information, South Valley University, Qena, Egypt
| | - Christoph Linse
- Institut für Neuro- und Bioinformatik, University of Lübeck, Lübeck, Germany
| | - Mohamed Abdalla
- Mathematics Department, Faculty of Science, King Khalid University, Abha, Saudi Arabia
- Mathematics Department, Faculty of Science, South Valley University, Qena, Egypt
| | - Erhardt Barth
- Institut für Neuro- und Bioinformatik, University of Lübeck, Lübeck, Germany
| | - Thomas Martinetz
- Institut für Neuro- und Bioinformatik, University of Lübeck, Lübeck, Germany
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Han L, Shen P, Yan J, Huang Y, Ba X, Lin W, Wang H, Huang Y, Qin K, Wang Y, Chen Z, Tu S. Exploring the Clinical Characteristics of COVID-19 Clusters Identified Using Factor Analysis of Mixed Data-Based Cluster Analysis. Front Med (Lausanne) 2021; 8:644724. [PMID: 34336871 PMCID: PMC8323882 DOI: 10.3389/fmed.2021.644724] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 06/23/2021] [Indexed: 12/22/2022] Open
Abstract
The COVID-19 outbreak has brought great challenges to healthcare resources around the world. Patients with COVID-19 exhibit a broad spectrum of clinical characteristics. In this study, the Factor Analysis of Mixed Data (FAMD)-based cluster analysis was applied to demographic information, laboratory indicators at the time of admission, and symptoms presented before admission. Three COVID-19 clusters with distinct clinical features were identified by FAMD-based cluster analysis. The FAMD-based cluster analysis results indicated that the symptoms of COVID-19 were roughly consistent with the laboratory findings of COVID-19 patients. Furthermore, symptoms for mild patients were atypical. Different hospital stay durations and survival differences among the three clusters were also found, and the more severe the clinical characteristics were, the worse the prognosis. Our aims were to describe COVID-19 clusters with different clinical characteristics, and a classifier model according to the results of FAMD-based cluster analysis was constructed to help provide better individualized treatments for numerous COVID-19 patients in the future.
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Affiliation(s)
- Liang Han
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Pan Shen
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Jiahui Yan
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Yao Huang
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Xin Ba
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Weiji Lin
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Hui Wang
- Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Ying Huang
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Kai Qin
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Yu Wang
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Zhe Chen
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Shenghao Tu
- Department of Integrated Chinese Traditional and Western Medicine, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
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Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip Sci 2021; 13:153-175. [PMID: 33886097 PMCID: PMC8060789 DOI: 10.1007/s12539-021-00431-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 04/03/2021] [Accepted: 04/09/2021] [Indexed: 12/23/2022]
Abstract
The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.
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Affiliation(s)
- Jawad Rasheed
- Department of Computer Engineering, Istanbul Aydin University, Istanbul, 34295, Turkey.
| | - Akhtar Jamil
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Alaa Ali Hameed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
| | - Ahmad Rasheed
- Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
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Ahmed KB, Goldgof GM, Paul R, Goldgof DB, Hall LO. Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:72970-72979. [PMID: 34178559 PMCID: PMC8224464 DOI: 10.1109/access.2021.3079716] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 05/04/2021] [Indexed: 05/02/2023]
Abstract
A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models have high accuracy on seen sources, but poor accuracy on unseen sources. The reason for the disparity is that the convolutional neural network model, which learns features, can focus on differences in X-ray machines or in positioning within the machines, for example. Any feature that a person would clearly rule out is called a confounding feature. Some of the models were trained on COVID-19 image data taken from publications, which may be different than raw images. Some data sets were of pediatric cases with pneumonia where COVID-19 chest X-rays are almost exclusively from adults, so lung size becomes a spurious feature that can be exploited. In this work, we have eliminated many confounding features by working with as close to raw data as possible. Still, deep learned models may leverage source specific confounders to differentiate COVID-19 from pneumonia preventing generalizing to new data sources (i.e. external sites). Our models have achieved an AUC of 1.00 on seen data sources but in the worst case only scored an AUC of 0.38 on unseen ones. This indicates that such models need further assessment/development before they can be broadly clinically deployed. An example of fine-tuning to improve performance at a new site is given.
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Affiliation(s)
- Kaoutar Ben Ahmed
- Department of Computer Science and EngineeringUniversity of South FloridaTampaFL33620USA
| | - Gregory M. Goldgof
- Department of Laboratory MedicineThe University of CaliforniaSan FranciscoCA94143USA
| | - Rahul Paul
- Department of Radiation OncologyMassachusetts General HospitalBostonMA02115USA
- Department of Radiation OncologyHarvard Medical SchoolBostonMA02115USA
| | - Dmitry B. Goldgof
- Department of Computer Science and EngineeringUniversity of South FloridaTampaFL33620USA
| | - Lawrence O. Hall
- Department of Computer Science and EngineeringUniversity of South FloridaTampaFL33620USA
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Soomro TA, Zheng L, Afifi AJ, Ali A, Yin M, Gao J. Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research. Artif Intell Rev 2021; 55:1409-1439. [PMID: 33875900 PMCID: PMC8047522 DOI: 10.1007/s10462-021-09985-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts' observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment .
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Affiliation(s)
- Toufique A. Soomro
- Department of Electronic Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Sindh Pakistan
| | - Lihong Zheng
- School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia
| | - Ahmed J. Afifi
- Computer Vision and Remote Sensing, Technische Universität Berlin, Berlin, Germany
| | - Ahmed Ali
- Eletrical Engineering Department, Sukkur IBA University, Sukkur, Pakistan
| | - Ming Yin
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Junbin Gao
- Discipline of Business Analytics in Business School, The University of Sydney, Sydney, Australia
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Rahimzadeh M, Attar A, Sakhaei SM. A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset. Biomed Signal Process Control 2021; 68:102588. [PMID: 33821166 PMCID: PMC8011666 DOI: 10.1016/j.bspc.2021.102588] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/28/2021] [Accepted: 03/26/2021] [Indexed: 12/13/2022]
Abstract
This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects. Our architecture applies a new feature pyramid network designed for classification problems to the ResNet50V2 model so the model becomes able to investigate different resolutions of the image and do not lose the data of small objects. As the infections of COVID-19 exist in various scales, especially many of them are tiny, using our method helps to increase the classification performance remarkably. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways on Xception, ResNet50V2, and our model. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient condition identification phase, the system correctly identified almost 234 of 245 patients with high speed. Our dataset is accessible at https://github.com/mr7495/COVID-CTset.
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Affiliation(s)
- Mohammad Rahimzadeh
- School of Computer Engineering, Iran University of Science and Technology, Iran
| | - Abolfazl Attar
- Department of Electrical Engineering, Sharif University of Technology, Iran
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Mohammad-Rahimi H, Nadimi M, Ghalyanchi-Langeroudi A, Taheri M, Ghafouri-Fard S. Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review. Front Cardiovasc Med 2021; 8:638011. [PMID: 33842563 PMCID: PMC8027078 DOI: 10.3389/fcvm.2021.638011] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 02/23/2021] [Indexed: 12/15/2022] Open
Abstract
Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.
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Affiliation(s)
- Hossein Mohammad-Rahimi
- Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohadeseh Nadimi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran, Iran
| | - Azadeh Ghalyanchi-Langeroudi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran, Iran
| | - Mohammad Taheri
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soudeh Ghafouri-Fard
- Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5577636. [PMID: 33859807 PMCID: PMC8009715 DOI: 10.1155/2021/5577636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/19/2021] [Accepted: 02/27/2021] [Indexed: 11/17/2022]
Abstract
Multilabel recognition of morphological images and detection of cancerous areas are difficult to locate in the scenario of the image redundancy and less resolution. Cancerous tissues are incredibly tiny in various scenarios. Therefore, for automatic classification, the characteristics of cancer patches in the X-ray image are of critical importance. Due to the slight variation between the textures, using just one feature or using a few features contributes to inaccurate classification outcomes. The present study focuses on five different algorithms for extracting features that can extract further different features. The algorithms are GLCM, LBGLCM, LBP, GLRLM, and SFTA from 8 image groups, and then, the extracted feature spaces are combined. The dataset used for classification is most probably imbalanced. Additionally, another focal point is to eradicate the unbalanced data problem by creating more samples using the ADASYN algorithm so that the error rate is minimized and the accuracy is increased. By using the ReliefF algorithm, it skips less contributing features that relieve the burden on the process. Finally, the feedforward neural network is used for the classification of data. The proposed method showed 99.5% micro, 99.5% macro, 0.5% misclassification, 99.5% recall rats, specificity 99.4%, precision 99.5%, and accuracy 99.5%, showing its robustness in these results. To assess the feasibility of the new system, the INbreast database was used.
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Ahmad F, Farooq A, Ghani MU. Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:8890226. [PMID: 33488691 PMCID: PMC7805527 DOI: 10.1155/2021/8890226] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 11/22/2020] [Accepted: 12/04/2020] [Indexed: 12/15/2022]
Abstract
The novel coronavirus, SARS-CoV-2, can be deadly to people, causing COVID-19. The ease of its propagation, coupled with its high capacity for illness and death in infected individuals, makes it a hazard to the community. Chest X-rays are one of the most common but most difficult to interpret radiographic examination for early diagnosis of coronavirus-related infections. They carry a considerable amount of anatomical and physiological information, but it is sometimes difficult even for the expert radiologist to derive the related information they contain. Automatic classification using deep learning models can help in better assessing these infections swiftly. Deep CNN models, namely, MobileNet, ResNet50, and InceptionV3, were applied with different variations, including training the model from the start, fine-tuning along with adjusting learned weights of all layers, and fine-tuning with learned weights along with augmentation. Fine-tuning with augmentation produced the best results in pretrained models. Out of these, two best-performing models (MobileNet and InceptionV3) selected for ensemble learning produced accuracy and FScore of 95.18% and 90.34%, and 95.75% and 91.47%, respectively. The proposed hybrid ensemble model generated with the merger of these deep models produced a classification accuracy and FScore of 96.49% and 92.97%. For test dataset, which was separately kept, the model generated accuracy and FScore of 94.19% and 88.64%. Automatic classification using deep ensemble learning can help radiologists in the correct identification of coronavirus-related infections in chest X-rays. Consequently, this swift and computer-aided diagnosis can help in saving precious human lives and minimizing the social and economic impact on society.
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Affiliation(s)
- Fareed Ahmad
- Department of Computer Science, University of Engineering and Technology, Lahore 54890, Pakistan
- Quality Operations Laboratory, Institute of Microbiology, University of Veterinary and Animal Sciences, Lahore, Pakistan
| | - Amjad Farooq
- Department of Computer Science, University of Engineering and Technology, Lahore 54890, Pakistan
| | - Muhammad Usman Ghani
- Department of Computer Science, University of Engineering and Technology, Lahore 54890, Pakistan
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