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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، آذر و 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
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