Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 18; no. 6; pp. 2775 - 2780
• Main Authors: Song, Ying, Zheng, Shuangjia, Li, Liang, Zhang, Xiang, Zhang, Xiaodong, Huang, Ziwang, Chen, Jianwen, Wang, Ruixuan, Zhao, Huiying, Chong, Yutian, Shen, Jun, Zha, Yunfei, Yang, Yuedong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bacteria; Case-Control Studies; China; Computational Biology; Computed tomography; Coronaviruses; COVID-19; COVID-19 - diagnosis; COVID-19 - diagnostic imaging; Deep Learning; Diagnosis; Diagnosis, Computer-Assisted - statistics & numerical data; Diagnosis, Differential; Feature extraction; Hospitals; Humans; Lesions; Lung; Medical diagnosis; Medical imaging; Models, Statistical; Opacity; Patients; Physicians; Pneumonia; pneumonia diagnosis; Pneumonia, Bacterial - diagnosis; Pneumonia, Bacterial - diagnostic imaging; Recall; SARS-CoV-2; Servers; Tomography, X-Ray Computed - statistics & numerical data; Viral diseases; weakly supervised learning; China
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2021.3065361

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 ).