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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

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Wang, Linda, Lin, Zhong Qiu, Wong, Alexander·2020·Scientific Reports

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Abstract

The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors’ knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors’ knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv · doi:10.48550/arxiv.1603.05027 · Cited by 10,665
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616

Show all 18 references

Can AI help in screening viral and COVID-19 pneumonia?
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3010287 · Cited by 1,913
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2987975 · Cited by 1,404
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,259
Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011-2022)
Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107161 · Cited by 734
Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717
Explainable Deep Learning Models in Medical Image Analysis
Singh, A., Sengupta, S., & Lakshminarayanan, V. (2020) · Journal of Imaging · doi:10.3390/jimaging6060052 · Cited by 718
A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2
Rahimzadeh, M., & Attar, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100360 · Cited by 695
AI for radiographic COVID-19 detection selects shortcuts over signal
DeGrave, A. J., Janizek, J. D., & Lee, S.-I. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00338-7 · Cited by 631
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays
Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105608 · Cited by 623
Machine-learning-based disease diagnosis: a comprehensive review
Ahsan, M. M., Luna, S. A., & Siddique, Z. (2022) · Healthcare · doi:10.3390/healthcare10030541 · Cited by 603
CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization
Mahmud, T., Rahman, M. A., & Fattah, S. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103869 · Cited by 525
Federated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
Horry, M. J., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., & Shukla, N. (2020) · IEEE Access · doi:10.1109/access.2020.3016780 · Cited by 472
AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app
Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100378 · Cited by 621
Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
Heidari, M., Mirniaharikandehei, S., Khuzani, A. Z., Danala, G., Qiu, Y., & Zheng, B. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104284 · Cited by 423
Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts
Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413
“Motivated by an important finding that infected COVID-19 patients normally reveal abnormalities in chest radiography images, the authors in [54] designed a deep convolutional neural network (CNN) model for the detection of COVID-19 cases.”
Background
Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection
Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., Sun, Z., He, J., Li, Y., Shen, C., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3040950 · Cited by 389
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
Naudé, W. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00978-0 · Cited by 368
Applications of Explainable Artificial Intelligence in Diagnosis and Surgery
Zhang, Y., Weng, Y., & Lund, J. (2022) · Diagnostics · doi:10.3390/diagnostics12020237 · Cited by 368
Deep Learning applications for COVID-19
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal of Big Data · doi:10.1186/s40537-020-00392-9 · Cited by 350
Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3206407 · Cited by 345
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks
Toraman, S., Alakus, T. B., & Turkoglu, I. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110122 · Cited by 327
A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization
Nour, M., Cömert, Z., & Polat, K. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106580 · Cited by 316
Federated learning for COVID-19 screening from Chest X-ray images
Feki, I., Ammar, S., Kessentini, Y., & Muhammad, K. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107330 · Cited by 278
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients
Shorfuzzaman, M., & Hossain, M. S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107700 · Cited by 217
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210
Unveiling COVID-19 from CHEST X-ray with deep learning: A hurdles race with small data
Tartaglione, E., Barbano, C. A., Berzovini, C., Calandri, M., & Grangetto, M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186933 · Cited by 211
DeepCOVID-XR: An artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large U.S. clinical data set
Wehbe, R. M., Sheng, J., Dutta, S., Chai, S., Dravid, A., Barutcu, S., Wu, Y., Cantrell, D. R., Xiao, N., Allen, B. D., et al. (2021) · Radiology · doi:10.1148/radiol.2020203511 · Cited by 190
Skin disease diagnosis with deep learning: a review
Li, H., Pan, Y., Zhao, J., & Zhang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.096 · Cited by 178
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105350 · Cited by 168
A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic
Sufian, A., Ghosh, A., Sadiq, A. S., & Smarandache, F. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101830 · Cited by 156
Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey
Maassen, O., Fritsch, S., Palm, J., Deffge, S., Kunze, J., Marx, G., Riedel, M., Schuppert, A., & Bickenbach, J. (2021) · Journal of Medical Internet Research · doi:10.2196/26646 · Cited by 125
Experiments of Federated Learning for COVID-19 Chest X-ray Images
Yan, B., Wang, J., Cheng, J., Zhou, Y., Zhang, Y., Yang, Y., Liu, L., Zhao, H., Wang, C., & Liu, B. (2021) · Communications in Computer and Information Science · doi:10.1007/978-3-030-78618-2_4 · Cited by 122
A systematic review on federated learning in medical image analysis
Sohan, M. F., & Basalamah, A. (2023) · IEEE Access · doi:10.1109/access.2023.3260027 · Cited by 114
COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., Shi, Q., Jin, S., Zhang, L., & You, Z. (2020) · arXiv · doi:10.48550/arxiv.2004.10987 · Cited by 109
Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds
Bagad, P., Dalmia, A., Doshi, J., Nagrani, A., Bhamare, P., Mahale, A., Rane, S., Agarwal, N., & Panicker, R. (2020) · arXiv · doi:10.48550/arxiv.2009.08790 · Cited by 91
Public Covid-19 X-ray datasets and their impact on model bias – A systematic review of a significant problem
Garcia Santa Cruz, B., Bossa, M. N., Sölter, J., & Husch, A. D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102225 · Cited by 89
let-7 microRNAs: Their Role in Cerebral and Cardiovascular Diseases, Inflammation, Cancer, and Their Regulation
Bernstein, D. L., Jiang, X., & Rom, S. (2021) · Biomedicines · doi:10.3390/biomedicines9060606 · Cited by 88
A systematic review on AI/ML approaches against COVID-19 outbreak
Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00424-8 · Cited by 87
A scoping review on multimodal deep learning in biomedical images and texts
Sun, Z., Lin, M., Zhu, Q., Xie, Q., Wang, F., Lu, Z., & Peng, Y. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104482 · Cited by 69
Deep Ensemble Model for Classification of Novel Coronavirus in Chest X‐Ray Images
Ahmad, F., Farooq, A., & Ghani, M. U. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/8890226 · Cited by 65
FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information
Ho, T.-T., Tran, K.-D., & Huang, Y. (2022) · Sensors · doi:10.3390/s22103728 · Cited by 44
Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers
Palatnik de Sousa, I., Vellasco, M. M. B. R., & Costa da Silva, E. (2021) · Sensors · doi:10.3390/s21165657 · Cited by 43
An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
Al-Timemy, A. H., Khushaba, R. N., Mosa, Z. M., & Escudero, J. (2021) · Studies in Systems, Decision and Control · doi:10.1007/978-3-030-69744-0_6 · Cited by 41
A Deep-Learning-Based Edge-Centric COVID-19-Like Pandemic Screening and Diagnosis System within a B5G Framework Using Blockchain
Muhammad, G., & Hossain, M. S. (2021) · IEEE Network · doi:10.1109/mnet.011.2000326 · Cited by 36
PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis
Song, M., Wang, J., Yu, Z., Wang, J., Yang, L., Lu, Y., Li, B., Wang, X., Wang, X., Huang, Q., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103248 · Cited by 31
Deep learning models for COVID-19 chest X-ray classification: preventing shortcut learning using feature Disentanglement
Trivedi, A., Robinson, C., Blazes, M., Ortiz, A., Desbiens, J., Gupta, S., Dodhia, R., Bhatraju, P. K., Liles, W. C., Kalpathy-Cramer, J., et al. (2022) · PLOS ONE · doi:10.1371/journal.pone.0274098 · Cited by 28
Untitled work
(2021) · doi:10.1145/3450439.3451862 · Cited by 28
Study on IoT for SARS-CoV-2 with healthcare: present and future perspective
Rahman, A., Department of Computer Science and Engineering, N. I. O. T. E. A. R. (., Rahman, M., Kundu, D., Karim, M. R., Band, S. S., Sookhak, M., Department of Computer Science and Engineering, M. B. S. A. T. U., Department of Electrical and Electronic Engineering, N. I. O. T. E. A. R. (., Future Technology Research Center, C. O. F., et al. (2021) · Mathematical Biosciences and Engineering · doi:10.3934/mbe.2021475 · Cited by 22
A Review of Generative Adversarial-Based Networks of Machine Learning/Artificial Intelligence in Healthcare
Suthar, A. C., Joshi, V., & Prajapati, R. (2022) · Advances in Computational Intelligence and Robotics · doi:10.4018/978-1-7998-8786-7.ch003 · Cited by 11
Automatic detection of COVID-19 from chest x-ray images using EfficientNet-B7 CNN model with channel-wise attention
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Authors: Wang, L., Lin, Z. Q., & Wong, A.
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2020-11-11
Volume: 10
Issue: 1
Pages: 19549-19549
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/s41598-020-76550-z
OpenAlex: W3105081694
Cited by: 3,113
References: 18
FWCI: 280.82
Type: journal article
Language: en
Open access: gold

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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

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