TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model.

Journal: Computers in biology and medicine
Published Date: Apr 16, 2022

Abstract

BACKGROUND: As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious capacity has further increased. Effective diagnosis of suspected cases is an important tool to stop the spread of the pandemic. Therefore, we intended to develop a computer-aided diagnosis system for the diagnosis of suspected cases.

Authors

  • Junding Sun
    School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China. Electronic address: sunjd@hpu.edu.cn.
  • Pengpeng Pi
    School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China. Electronic address: pipengpeng@home.hpu.edu.cn.
  • Chaosheng Tang
    School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China. Electronic address: tcs@hpu.edu.cn.
  • Shui-Hua Wang
    School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, United Kingdom.
  • Yu-Dong Zhang
    University of Leicester, Leicester, United Kingdom.

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