COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning

Kwang Jin Park, Young Jin Choi, Hong Chul Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The infectious coronavirus disease-19 (COVID-19) is a viral disease that affects the lungs, which caused great havoc when the epidemic rapidly spread around the world. Polymerase chain reaction (PCR) tests are conducted to screen for COVID-19 and respond to quarantine measures. However, PCR tests take a considerable amount of time to confirm the test results. Therefore, to supplement the accuracy and quickness of a COVID-19 diagnosis, we proposed an effective deep learning methodology as a quarantine response through COVID-19 chest X-ray image classification based on domain extension transfer learning. As part of the data preprocessing, contrast limited adaptive histogram equalization was applied to chest X-ray images using Medical Information Mart for Intensive Care (MIMIC)-IV obtained from the Beth Israel Deaconess Medical Center. The classification of the COVID-19 X-ray images was conducted using a pretrained ResNet-50. We also visualized and interpreted the classification performance of the model through explainable artificial intelligence and performed statistical tests to validate the reliability of the model. The proposed method correctly classified images with 96.7% accuracy, an improvement of about 9.9% over the reference model. This study is expected to help medical staff make an integrated decision in selecting the first confirmed case and contribute to suppressing the spread of the virus in the community.

Original languageEnglish
Article number10715
JournalApplied Sciences (Switzerland)
Volume12
Issue number21
DOIs
Publication statusPublished - 2022 Nov

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Keywords

  • COVID-19
  • chest X-ray
  • domain extension transfer learning
  • explainable artificial intelligence
  • image classification

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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