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

Ali Abbasian Ardakani, Alireza Rajabzadeh Kanafi, U Rajendra Acharya, Nazanin Khadem, Afshin Mohammadi, Ali Abbasian Ardakani, Alireza Rajabzadeh Kanafi, U Rajendra Acharya, Nazanin Khadem, Afshin Mohammadi

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%; 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.

Keywords: COVID-19; Computed tomography; Coronavirus infections; Deep learning; Lung diseases; Machine learning; Pneumonia.

Conflict of interest statement

Declaration of competing interest None.

Copyright © 2020 Elsevier Ltd. All rights reserved.

Figures

Fig. 1
Fig. 1
CT sample images of patients with pneumonia. (a): A 28-year-old male with confirmed COVID-19 pneumonia. The red arrows indicate ground-glass opacity in the right and left lower lobes. (b): A 33-year-old female patient with confirmed COVID-19 pneumonia. The red and yellow arrows indicate typical mixed ground-glass opacity-consolidation patterns in both lobes, and a ground-glass opacity pattern in the peripheral right upper lobe, respectively. (c): An 81-year-old female patient with H1N1 influenza. The red arrows indicate infection with mixed ground-glass opacity and consolidation pattern in both lobes (d): A 72-year-old male patient with atypical pneumonia. The red arrows indicate ground-glass opacity in the right middle lobe.
Fig. 2
Fig. 2
CT sample patch images of patients with COVID-19 (a) and other atypical and viral pneumonia diseases (b).
Fig. 3
Fig. 3
An overview of the ten pre-trained networks architecture used in this study. In each network, the convolution layers with the same color are in the same size. The straight arrows represent the direction of flow and computation. All convolution layers of the AlexNet, VGG-16 and VGG-19 networks are depicted. However, for the other networks, convolution layers of the most repetitive (core) part are depicted.
Fig. 4a
Fig. 4a
(a)ROC curves of individual ten networks and the radiologist on validating dataset.
Fig. 4b
Fig. 4b
(b)Radar plot of individual ten networks and the radiologist on validating dataset.
Fig. 5
Fig. 5
Accuracy (green line) and loss (red line) plot of ten convolutional neural network for training and validating datasets: (a), AlexNet; (b), VGG-16; (c), VGG-19; (d), SqueezeNet; (e), GoogleNet; (f), MobileNet-V2; (g), ResNet-18; (h), ResNet-50; (i), ResNet-101; (j), Xception.
Fig. 5
Fig. 5
Accuracy (green line) and loss (red line) plot of ten convolutional neural network for training and validating datasets: (a), AlexNet; (b), VGG-16; (c), VGG-19; (d), SqueezeNet; (e), GoogleNet; (f), MobileNet-V2; (g), ResNet-18; (h), ResNet-50; (i), ResNet-101; (j), Xception.

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