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Mohammadi, R, Salehi, M, Ghaffari, H, Rohani, A A, Reiazi, R. (1399). Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images. سامانه مدیریت نشریات علمی, 10(5), 559-568. doi: 10.31661/jbpe.v0i0.2008-1153
R Mohammadi; M Salehi; H Ghaffari; A A Rohani; R Reiazi. "Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images". سامانه مدیریت نشریات علمی, 10, 5, 1399, 559-568. doi: 10.31661/jbpe.v0i0.2008-1153
Mohammadi, R, Salehi, M, Ghaffari, H, Rohani, A A, Reiazi, R. (1399). 'Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images', سامانه مدیریت نشریات علمی, 10(5), pp. 559-568. doi: 10.31661/jbpe.v0i0.2008-1153
Mohammadi, R, Salehi, M, Ghaffari, H, Rohani, A A, Reiazi, R. Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images. سامانه مدیریت نشریات علمی, 1399; 10(5): 559-568. doi: 10.31661/jbpe.v0i0.2008-1153

Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images

Journal of Biomedical Physics and Engineering
مقاله 3، دوره 10، شماره 5، دی 2020، صفحه 559-568    اصل مقاله (1.16 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2008-1153
نویسندگان
R Mohammadi1؛ M Salehi* 1؛ H Ghaffari1؛ A A Rohani2؛ R Reiazi3، 4، 5
1MSc, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
2MSc, Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
3PhD, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
4PhD, Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
5PhD, Department of Medical Biophysics, University of Toronto, Toronto, Canada
چکیده
Background: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease and global health crisis. Although real-time reverse transcription polymerase chain reaction (RT-PCR) is known as the most widely laboratory method to detect the COVID-19 from respiratory specimens. It suffers from several main drawbacks such as time-consuming, high false-negative results, and limited availability. Therefore, the automatically detect of COVID-19 will be required.
Objective: This study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection of COVID-19 infection in chest X-rays.
Material and Methods: In a retrospective study, we have applied Visual Geometry Group (VGG)-16, VGG-19, MobileNet, and InceptionResNetV2 pre-trained models for detection COVID-19 infection from 348 chest X-ray images.
Results: Our proposed models have been trained and tested on a dataset which previously prepared. The all proposed models provide accuracy greater than 90.0%. The pre-trained MobileNet model provides the highest classification performance of automated COVID-19 classification with 99.1% accuracy in comparison with other three proposed models. The plotted area under curve (AUC) of receiver operating characteristics (ROC) of VGG16, VGG19, MobileNet, and InceptionResNetV2 models are 0.92, 0.91, 0.99, and 0.97, respectively. 
Conclusion: The all proposed models were able to perform binary classification with the accuracy more than 90.0% for COVID-19 diagnosis. Our data indicated that the MobileNet can be considered as a promising model to detect COVID-19 cases. In the future, by increasing the number of samples of COVID-19 chest X-rays to the training dataset, the accuracy and robustness of our proposed models increase further.
کلیدواژه‌ها
COVID-19؛ Transfer Learning؛ X-ray Images؛ Deep Learning؛ Convolution Neural Network؛ Machine Learning
سایر فایل های مرتبط با مقاله
مراجع
  1. Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, et al. Lung infection quantification of covid-19 in ct images with deep learning. ArXiv. 2020.
  2. Worldometer. COVID-19 CORONAVIRUS PANDEMIC. [Cited 2020 July 28]. 2020. Available from: https://www.worldometers.info/coronavirus/?utm_campaign=homeAdvegas1?
  3. Yuki K, Fujiogi M, Koutsogiannaki S. COVID-19 pathophysiology: A review. Clin Immunol. 2020; 215:108427. doi: 10.1016/j.clim.2020.108427. PubMed PMID: 32325252. PubMed PMCID: PMC7169933.
  4. Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, et al. Detection of SARS-CoV-2 in Different Types of Clinical Specimens. JAMA. 2020;323(18):1843-4. doi: 10.1001/jama.2020.3786. PubMed PMID: 32159775. PubMed PMCID: PMC7066521.
  5. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020;296(2):E32-40. doi: 10.1148/radiol.2020200642. PubMed PMID: 32101510. PubMed PMCID: PMC7233399.
  6. Sethy PK, Behera SK. Detection of Coronavirus Disease (COVID-19) Based on Deep Features. Preprints 2020. doi: 10.20944/preprints202003.0300.v1.
  7. Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. ArXiv. 2020.
  8. Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, et al. Coronavirus Disease 2019 (COVID-19): A Perspective from China. Radiology. 2020;296(2):E15-25. doi: 10.1148/radiol.2020200490. PubMed PMID: 32083985. PubMed PMCID: PMC7233368.
  9. Kanne JP, Little BP, Chung JH, Elicker BM, Ketai LH. Essentials for Radiologists on COVID-19: An Update-Radiology Scientific Expert Panel. Radiology. 2020;296(2):E113-4. doi: 10.1148/radiol.2020200527. PubMed PMID: 32105562. PubMed PMCID: PMC7233379.
  10. Basavegowda HS, Dagnew G. Deep learning approach for microarray cancer data classification. CAAI Trans Intell Technol. 2020;5(1):22-33. doi: 10.1049/trit.2019.0028.
  11. Majeed T, Rashid R, Ali D, Asaad A. Problems of Deploying CNN Transfer Learning to Detect COVID-19 from Chest X-rays. MedRxiv. 2020.
  12. Kaur M, Singh D. Fusion of medical images using deep belief networks. Cluster Comput. 2020;23:1439-53. doi: 10.1007/s10586-019-02999-x.
  13. Qi G, Wang H, Haner M, Weng C, Chen S, Zhu Z. Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation. CAAI Trans Intell Technol. 2019;4(2):80-91. doi: 10.1049/trit.2018.1045.
  14. Shukla PK, Sharma P, Rawat P, Samar J, Moriwal R, et al. Efficient prediction of drug–drug interaction using deep learning models. IET Syst Biol. 2020;14:211-6.
  15. Sarker L, Islam MM, Hannan T,  Ahmed Z. COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images. Preprints. 2020. doi: 10.20944/preprints202005.0151.v1.
  16. Hussain M, Bird JJ, Faria DR. A Study on CNN Transfer Learning for Image Classification. Springer International Publishing: Advances in Computational Intelligence Systems; 2018. p. 191-202.
  17. Rahimzadeh M, Attar A. Introduction of a new Dataset and Method for Detecting and Counting the Pistachios based on Deep Learning. ArXiv. 2020.
  18. Lih OS, Jahmunah V, San TR, Ciaccio EJ, Yamakawa T, Tanabe M, et al. Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med. 2020;103:101789. doi: 10.1016/j.artmed.2019.101789. PubMed PMID: 32143796.
  19. Dekhtiar J, Durupt A, Bricogne M, Eynard B, Rowson H, Kiritsis D. Deep learning for big data applications in CAD and PLM – Research review, opportunities and case study. Comput Ind. 2018;100:227-43. doi: 10.1016/j.compind.2018.04.005.
  20. Guo T, Dong J, Li H, Gao Y, editors. Simple convolutional neural network on image classification. 2nd International Conference on Big Data Analysis (ICBDA); Beijing, China: IEEE; 2017. doi: 10.1109/ICBDA.2017.8078730.
  21. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv. 2014.
  22. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. ArXiv. 2017.
  23. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. Conference on Computer Vision and Pattern Recognition (CVPR); Boston, MA, USA: IEEE; 2015. p. 1-9. doi: 10.1109/CVPR.2015.7298594.
  24. Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M. Covid-19 image data collection: Prospective predictions are the future. ArXiv. 2020.
  25. Streiner DL, Cairney J. What’s under the ROC? An introduction to receiver operating characteristics curves. Can J Psychiatry. 2007;52(2):121-8. doi: 10.1177/070674370705200210.
  26. Hemdan EE-D, Shouman MA, Karar ME. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. ArXiv. 2020.
  27. Wang L, Wong A. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. ArXiv. 2020.
  28. Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43:635-40. doi: 10.1007/s13246-020-00865-4.
  29. Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). MedRxiv. 2020. doi: 10.1101/2020.02.14.20023028.
  30. Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, et al. Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label. MedRxiv. 2020. doi: 10.1101/2020.03.12.20027185.
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