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. 2023 Mar 3:13:1116285.
doi: 10.3389/fcimb.2023.1116285. eCollection 2023.

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling

Hong-Tao Zhang  1 , Ze-Yu Sun  2 , Juan Zhou  1 , Shen Gao  1 , Jing-Hui Dong  1 , Yuan Liu  1 , Xu Bai  1 , Jin-Lin Ma  1 , Ming Li  1 , Guang Li  2 , Jian-Ming Cai  1 , Fu-Geng Sheng  1
Affiliations

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling

Hong-Tao Zhang et al. Front Cell Infect Microbiol. .

Abstract

Background: There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases.

Methods: A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases.

Results: The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists.

Conclusions: This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

Keywords: artificial intelligence; computed tomography (CT); coronavirus disease 2019 (COVID-19); deep learning; global average pooling (GAP).

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Conflict of interest statement

Author Z-YS and GL were employed by Keya Medical Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Workflow chart of the whole COVID-19 triage algorithm. There are the following modules: pre-process, lung segmentation, and classification. A 3d-Unet model was used for lung segmentation and the input of this module is the isotropic volume after pre-process module. The cropped isotropic image by the lung mask region is the input of classification module. Before the classifier, a weight-mask GAP is applied to the feature maps extracted by resnet-50 model.
Figure 2
Figure 2
CT images and related mask. First raw are original CT image, second raw are the corresponding segmentation results. In the figure, the background is shown in black, the right lung is shown in blue, and the left lung is shown in green.
Figure 3
Figure 3
The ROC cure comparison of the two experiment results of resnet50 with GAP and with mask weighted GAP. Vertical axis is false positive rate, while horizontal axis is true positive rate. The blue cure is the result of mask weighted GAP, while the red cure is the result of GAP.
Figure 4
Figure 4
Fusion images with Grad-CAM, which indicate the import region for classification. First row are normal cases, second row are COVID-19 cases. RGB color indicate the high risk for suspects, light color indicate the normal region.
See this image and copyright information in PMC

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