Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks
(چکیده مقاله) :
Abstract :
Fast diagnostic methods can control and prevent the spread of pandemic diseases like
coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high
workload conditions. Although a laboratory test is the current routine diagnostic tool, it is
time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis.
Computed tomography (CT) has thus far become a fast method to diagnose patients with
COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was
moderate. Accordingly, additional investigations are needed to improve the performance in
diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19
diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients
with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical
and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known
convolutional neural networks were used to distinguish infection of COVID-19 from non-
COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2,
ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best
performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish
COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity,
99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%;
specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was
moderate with an AUC of 0.873 (sensitivity, 89.21
coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high
workload conditions. Although a laboratory test is the current routine diagnostic tool, it is
time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis.
Computed tomography (CT) has thus far become a fast method to diagnose patients with
COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was
moderate. Accordingly, additional investigations are needed to improve the performance in
diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19
diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients
with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical
and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known
convolutional neural networks were used to distinguish infection of COVID-19 from non-
COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2,
ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best
performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish
COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity,
99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%;
specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was
moderate with an AUC of 0.873 (sensitivity, 89.21
(توضیحات تکمیلی) :
(توضیحات تکمیلی) :
Description :
مقاله ISI انگلیسی اصلی
سال انتشار: 2020
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 30 صفحه
سال انتشار: 2020
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 30 صفحه
Authors / Descriptions(نویسندگان/توضیحات): سال انتشار 2020 \ مقاله ISI انگلیسی اصلی \ نویسندگان: Ali Abbasian Ardakani, Alireza Rajabzadeh Kanafi, U. Rajendra Acharya, Nazanin Khadem, Afshin Mohammadi
Sent date(تاریخ ارسال) :
1399/02/11 | 4/30/2020
Number of visits(تعداد بازدید):
811
Key words (کلمات کلیدی):
Computed tomography; Coronavirus Infections; COVID-19; Deep learning; Lung Diseases; Pneumonia; Machine learning
Number of pages(تعداد صفحات) :
30
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