Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.

Journal: Computers in biology and medicine
PMID:

Abstract

BACKGROUND: The coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial.

Authors

  • Yanan Wu
    School of Physics and Optoelectronic Engineering, Ludong University, Yantai, Shandong 264025, China.
  • Qianqian Qi
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China. Electronic address: 386489753@qq.com.
  • Shouliang Qi
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Life Science Building, 500 Zhihui Street, Hun'nan District, Shenyang, 110169, China. qisl@bmie.neu.edu.cn.
  • Liming Yang
    College of Science, China Agricultural University, 100083, Beijing, China. Electronic address: cauyanglm@163.com.
  • Hanlin Wang
    Department of Radiology, General Hospital of the Yangtze River Shipping, Wuhan, China. Electronic address: 75288763@qq.com.
  • Hui Yu
    Engineering Technology Research Center of Shanxi Province for Opto-Electric Information and Instrument, Taiyuan 030051, China. 13934603474@nuc.edu.cn.
  • Jianpeng Li
    Department of Cardiology, Taizhou Second People's Hospital, The Affiliated Taizhou Second People's Hospital of Yangzhou University, Taizhou, China.
  • Gang Wang
    National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
  • Ping Zhang
    Department of Computer Science and Engineering, The Ohio State University, USA.
  • Zhenyu Liang
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. Electronic address: 490458234@qq.com.
  • Rongchang Chen
    Key Laboratory of Respiratory Disease of Shenzhen, Shenzhen Institute of Respiratory Disease, Shenzhen People's Hospital (Second Affiliated Hospital of Jinan University, First Affiliated Hospital of South University of Science and Technology of China), Shenzhen, China. Electronic address: chenrc@vip.163.com.