期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 18, 期 6, 页码 2775-2780出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3065361
关键词
Feature extraction; Computed tomography; Lung; COVID-19; Hospitals; Lesions; Deep learning; Medical diagnosis; COVID-19; deep learning; pneumonia diagnosis; weakly supervised learning
类别
资金
- National Key R&D 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, China [2019A1515012207]
- Introducing Innovative and Entrepreneurial Teams [2016ZT06D211]
The emergence of COVID-19 has led to the urgent need for accurate diagnoses through CT scans. Researchers have developed a deep learning-based CT diagnosis system to accurately identify COVID-19 patients, achieving high levels of accuracy and reliability.
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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