Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.,IEEE/ACM Transactions on Computational Biology and Bioinformatics

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Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 4.5 ) Pub Date : 2021-03-11 , DOI: 10.1109/tcbb.2021.3065361
Ying Song , Shuangjia Zheng , Liang Li , Xiang Zhang , Xiaodong Zhang , Ziwang Huang , Jianwen Chen , Ruixuan Wang , Huiying Zhao , Yutian Chong , Jun Shen , Yunfei Zha , Yuedong Yang

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 http://biomed.nscc-gz.cn/model.php.

更新日期：2021-03-11

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