COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.

Journal: Interdisciplinary sciences, computational life sciences
PMID: 32959234

Abstract

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce pulmonary failures, and chest X-ray imaging becomes one of the major confirmed diagnostic technologies. The very limited number of publicly available samples has rendered the training of the deep neural networks unstable and inaccurate. This study proposed a two-step transfer learning pipeline and a deep residual network framework COVID19XrayNet for the COVID-19 detection problem based on chest X-ray images. COVID19XrayNet firstly tunes the transferred model on a large dataset of chest X-ray images, which is further tuned using a small dataset of annotated chest X-ray images. The final model achieved 0.9108 accuracy. The experimental data also suggested that the model may be improved with more training samples being released. COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.

Authors

  • Ruochi Zhang
    BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
  • Zhehao Guo
    School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
  • Yue Sun
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
  • Qi Lu
    Key Laboratory for Optoelectronic Technology and System of the Education Ministry of China, College of Optoelectronic Engineering, Chongqing University, Chongqing, China.
  • Zijian Xu
    School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
  • Zhaomin Yao
    BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
  • Meiyu Duan
    BioKnow Health Informatics Lab, College of Computer Science and Technology.
  • Shuai Liu
    Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China.
  • Yanjiao Ren
    College of Information Technology, Jilin Agricultural University, Changchun, 130118, Jilin, China.
  • Lan Huang
  • Fengfeng Zhou

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