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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 3.6 ) 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
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 3.6 ) 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) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak 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. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. 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), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (
http://biomed.nscc-gz.cn/model.php
). Source codes and datasets are available at our GitHub (
https://github.com/SY575/COVID19-CT
).
更新日期：2021-03-11
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