Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

• Published in: Computers in biology and medicine Vol. 121; p. 103795
• Main Authors: Ardakani, Ali Abbasian, Kanafi, Alireza Rajabzadeh, Acharya, U. Rajendra, Khadem, Nazanin, Mohammadi, Afshin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2020; Elsevier Limited
• Subjects: Accuracy; Adult; Aged; Aged, 80 and over; Artificial Intelligence; Artificial neural networks; Betacoronavirus; Clinical medicine; Computational Biology; Computed tomography; Control methods; Coronavirus infections; Coronavirus Infections - diagnosis; Coronavirus Infections - diagnostic imaging; Coronaviruses; Cost analysis; COVID-19; Deep Learning; Diagnosis; Diagnostic software; Diagnostic systems; Disease control; Female; Humans; Infections; Internal Medicine; Laboratories; Laboratory tests; Lung - diagnostic imaging; Lung diseases; Machine learning; Male; Middle Aged; Neural networks; Neural Networks, Computer; Other; Pandemics; Performance enhancement; Physicians; Pneumonia; Pneumonia - diagnosis; Pneumonia - diagnostic imaging; Pneumonia, Viral - diagnosis; Pneumonia, Viral - diagnostic imaging; Radiographic Image Interpretation, Computer-Assisted; Radiology; Respiratory diseases; SARS-CoV-2; Sensitivity; Tomography, X-Ray Computed; Viral diseases; COVID-19; Deep learning; Pneumonia; Coronavirus infections; Computed tomography; Lung diseases; Machine learning
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2020.103795

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%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments. •Ten CNNs were used to distinguish infection of COVID-19 from non-COVID-19 groups.•ResNet-101 and Xception represented the best performance with an AUC of 0.994.•Deep learning technique can be used as an adjuvant tool in diagnosing COVID-19.