ArticleFrontiers in artificial intelligence2024
Uncertainty quantification in multi-class image classification using chest X-ray images of COVID-19 and pneumonia.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Uncertainty-Aware adaptive neuro-fuzzy transformer framework for robust multi-center lung disease classification.Scientific reports · 2026Article
- An explainable ResNet50-BiLSTM-attention framework with spatial token modeling and imbalance-aware learning for multi-class knee osteoarthritis severity grading.Frontiers in medicine · 2026Article
- Sureness of classification of breast cancers as pure ductal carcinomaJournal of medical imaging (Bellingham, Wash.) · 2025Article
- The Hidden Threat of Hallucinations in Binary Chest X-ray Pneumonia Classification.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
This paper investigates uncertainty quantification (UQ) techniques in multi-class classification of chest X-ray images (COVID-19, Pneumonia, and Normal). We evaluate Bayesian Neural Networks (BNN) and the Deep Neural Network with UQ (DNN with UQ) techniques, including Monte Carlo dropout, Ensemble Bayesian Neural Network (EBNN), Ensemble Monte Carlo (EMC) dropout, across different evaluation metrics. Our analysis reveals that DNN with UQ, especially EBNN and EMC dropout, consistently outperform BNNs. For example, in Class 0 vs. All, EBNN achieved a
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