Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability

Waleed Aldhahi, Sanghoon Sull

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

The ongoing coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on patients and healthcare systems across the world. Distinguishing non-COVID-19 patients from COVID-19 patients at the lowest possible cost and in the earliest stages of the disease is a major issue. Additionally, the implementation of explainable deep learning decisions is another issue, especially in critical fields such as medicine. The study presents a method to train deep learning models and apply an uncertainty-based ensemble voting policy to achieve 99% accuracy in classifying COVID-19 chest X-rays from normal and pneumonia-related infections. We further present a training scheme that integrates the cyclic cosine annealing approach with cross-validation and uncertainty quantification that is measured using prediction interval coverage probability (PICP) as final ensemble voting weights. We also propose the Uncertain-CAM technique, which improves deep learning explainability and provides a more reliable COVID-19 classification system. We introduce a new image processing technique to measure the explainability based on ground-truth, and we compared it with the widely adopted Grad-CAM method.

Original languageEnglish
Article number441
JournalDiagnostics
Volume13
Issue number3
DOIs
Publication statusPublished - 2023 Feb

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • COVID-19
  • deep learning
  • explainable AI
  • intelligent signal processing
  • uncertainty

ASJC Scopus subject areas

  • Clinical Biochemistry

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