An interpretable multi-task system for clinically applicable COVID-19 diagnosis using CXR.

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2022

Abstract

BACKGROUND: With the emergence of continuously mutating variants of coronavirus, it is urgent to develop a deep learning model for automatic COVID-19 diagnosis at early stages from chest X-ray images. Since laboratory testing is time-consuming and requires trained laboratory personal, diagnosis using chest X-ray (CXR) is a befitting option.

Authors

  • Yan Zhuang
    Medical Psychology Department, Taiyuan Mental Hospital, Taiyuan, China.
  • Md Fashiar Rahman
    Department of Industrial, Manufacturing & Systems Engineering, The University of Texas at El Paso, El Paso, TX, USA.
  • Yuxin Wen
    Fowler School of Engineering, Chapman University, Orange, CA, United States.
  • Michael Pokojovy
    Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX, USA.
  • Peter McCaffrey
    University of Texas Medical Branch, Galveston, TX, USA. pemccaff@UTMB.EDU.
  • Alexander Vo
    University of Texas Medical Branch, Galveston, TX, USA.
  • Eric Walser
    Department of Radiology, Department of Surgery, University of Texas Medical Branch, Galveston, TX, USA.
  • Scott Moen
    University of Texas Medical Branch, Galveston, TX, USA.
  • Honglun Xu
    Department of Industrial, Manufacturing & Systems Engineering, The University of Texas at El Paso, El Paso, TX, USA.
  • Tzu-Liang Bill Tseng
    Department of Industrial, Manufacturing and Systems Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA.

Keywords

External Resources

Popular Topics
Recent Journals