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Cited by 957

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

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

Open access
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·2021·IEEE/ACM Transactions on Computational Biology and Bioinformatics
DOI 10.1109/tcbb.2021.3065361

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Abstract

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).

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Cites

Research works printed but not linked: 13

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Cited by

See all 957

Metadata

Authors
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Wang, R., Zhao, H., Chong, Y., Shen, J., Zha, Y., & Yang, Y.
Venue
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021-11-01
Volume
18
Issue
6
Pages
2775-2780
ISSN
  • 1545-5963 (ISSN-L)
  • 1557-9964
  • 2374-0043
DOI
10.1109/tcbb.2021.3065361
OpenAlex
W3135243128
Cited by
957
References
9
FWCI
93.05
Type
journal article
Language
en
Open access
green
Licence
Funders
  • National Key Research and Development Program of China (2018YFC1315405)
  • National Natural Science Foundation of China (U1611261, 61772566, 81801132, 81871332)
  • Guangdong Frontier and Key Tech Innovation Program (2018B010109006, 2019B020228001)
  • Natural Science Foundation of Guangdong Province (2019A1515012207)
  • Introducing Innovative and Entrepreneurial Teams (2016ZT06D211)

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