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Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images
Ying Song; Shuangjia Zheng; Liang Li; Xiang Zhang; Xiaodong Zhang; Ziwang Huang; Jianwen Chen; Huiying Zhao; Yusheng Jie; Ruixuan Wang; Yutian Chong; Jun Shen; Yunfei Zha; Yuedong Yang.
Affiliation
  • Ying Song; National Supercomputer Center in Guangzhou, Sun Yat-sen University, Guangzhou 510006, China
  • Shuangjia Zheng; National Supercomputer Center in Guangzhou, Sun Yat-sen University, Guangzhou 510006, China
  • Liang Li; Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
  • Xiang Zhang; Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510530, China
  • Xiaodong Zhang; Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510220, China
  • Ziwang Huang; School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
  • Jianwen Chen; School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
  • Huiying Zhao; Sun Yat-Sen Memorial Hospital
  • Yusheng Jie; Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University
  • Ruixuan Wang; School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
  • Yutian Chong; Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510220, China
  • Jun Shen; Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510530, China
  • Yunfei Zha; Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
  • Yuedong Yang; School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
Preprint in English | medRxiv | ID: ppmedrxiv-20026930
Journal article
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ABSTRACT
BackgroundA 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. Materials and MethodsWe 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. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. ResultsThe experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http//biomed.nscc-gz.cn/server/Ncov2019. ConclusionsThe established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.
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Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document type: Preprint
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