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Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability.
- Source :
-
Diagnostics (2075-4418) . Feb2023, Vol. 13 Issue 3, p441. 22p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *VOTING machines
*COVID-19
*CHEST X rays
*DEEP learning
*IMAGE processing
Subjects
Details
- Language :
- English
- ISSN :
- 20754418
- Volume :
- 13
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Diagnostics (2075-4418)
- Publication Type :
- Academic Journal
- Accession number :
- 161819910
- Full Text :
- https://doi.org/10.3390/diagnostics13030441