Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

Journal: Frontiers in cellular and infection microbiology
Published Date: Mar 3, 2023

Abstract

BACKGROUND: There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases.

Authors

  • Hong-Tao Zhang
    Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
  • Ze-Yu Sun
    Algorithm Center, Keya Medical Technology Co., Ltd, Shenzhen, China.
  • Juan Zhou
    Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
  • Shen Gao
    Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
  • Jing-Hui Dong
    Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
  • Yuan Liu
    Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
  • Xu Bai
    Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
  • Jin-Lin Ma
    Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
  • Ming Li
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
  • Guang Li
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
  • Jian-Ming Cai
    Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
  • Fu-Geng Sheng
    Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.

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