Scientific Reports (Aug 2021)

Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection

  • Jie Hou,
  • Terry Gao

DOI
https://doi.org/10.1038/s41598-021-95680-6
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 15

Abstract

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Abstract To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.