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A Deep Learning Based Method For COVID-19 Classification Using Chest CT Images

At the beginning of 2020, coronavirus disease 2019(COVID-19) infection spread in Wuhan, China and all over the world. Until April, it had affected millions of people. The computed tomography (CT) imaging is confirmed as one of the assessment method for COVID-19 patients. However distinguish the COVID-19 from those CT images is extremely challenging as it is very time-consuming, and lack of the experienced radiologists. So deep learning based approaches are proposed to triage the COVID-19 images from the normal or other pneumonia images. Here, we proposed a novel global average pooling (GAP) method for the deep neural network to improve the performance of the COVID-19 classification. The novel GAP method is using lung mask region as weighting factor for GAP, which reduce the influence of background region and highlight the classification features of interesting tissue region. The result of our method achieved the triage of COVID-19 with sensitivity 96.4 % and specificity 93.3 % on the independence validation dataset with 2062 CT scans.