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In IEEE/ACM transactions on computational biology and bioinformatics

A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. A deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model can accurately identify the COVID-19 patients from the healthy with an AUC of 0.99, recall (sensitivity) of 0.93, and precision of 0.96. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO) that is visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by

Song Ying, Zheng Shuangjia, Li Liang, Zhang Xiang, Zhang Xiaodong, Huang Ziwang, Chen Jianwen, Wang Ruixuan, Zhao Huiying, Zha Yunfei, Shen Jun, Chong Yutian, Yang Yuedong