Characterization of Pneumonia Diagnostic Uncertainty: A Case Study on The CheXpert Dataset

Pneumonia is a serious respiratory infection that presents significant diagnostic challenges due to the variability in its symptoms and its overlap with other respiratory diseases. This study investigates the potential of diagnostic uncertainty labels to enhance CAD system's pneumonia classification. Specifically, it explores the feasibility of a ternary classification approach (classifying X-rays as positive, negative, or uncertain), introducing uncertainty as a distinct diagnostic category, aiming to provide a more nuanced and cautious classification of pneumonia. Data processing techniques, including undersampling to balance classes, image resizing, and data augmentation, were applied. Transfer learning with the CheXNet model was then employed in a Monte Carlo cross-validation framework across 16 random data splits. The ROC curves and the areas under the ROC curves for the uncertainty class were analyzed, challenging the notion that uncertainty cannot be effectively characterized. The results indicated a degree of class separation, indicating that the uncertainty carried enough information to be characterized and suggesting the viability of the envisioned ternary model. Additionally, due to the exclusive use of frontal view X-rays and application of undersampling, results are expected to be further improved in future research.