Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2020

Abstract

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system.

Authors

  • Md Mamunur Rahaman
    Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Yudong Yao
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China.
  • Frank Kulwa
    Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
  • Mohammad Asadur Rahman
    Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
  • Qian Wang
    Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
  • 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.
  • Fanjie Kong
    Electrical Engineering Department, Pratt School of Engineering Duke University, Durham, NC, USA.
  • Xuemin Zhu
    Whiting School of Engineering, Johns Hopkins University, 500 W University Parkway, MD, USA, USA.
  • Xin Zhao
    Florida International University.

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