Performance-interpretability trade-offs and generalization in deep learning for pneumonia detection: A benchmarking study

Abstract

Chest X-ray imaging remains the most widely used radiological modality for pneumonia screening. While recent advances in deep learning have demonstrated strong diagnostic performance, the deployment of such models in real-world settings requires not only high accuracy but also robustness and interpretability across different model designs and populations. In this work, we present a comprehensive benchmarking study of multiple deep learning architectures for pneumonia detection. A distinctive methodological feature of this study is the use of two demographically distinct datasets: a pediatric chest X-ray dataset for training, and an independent adult population dataset for external validation. All models were evaluated under a standardized cross-validated protocol. Beyond predictive metrics, we conduct an extensive eXplainable Artificial Intelligence (XAI) analysis assessing both qualitative and quantitative properties, including explanation stability and localization fidelity. Results show that model design choices significantly influence both predictive performance and explanation behavior. In particular, certain architectures consistently achieved superior performance while producing more focused and stable explanations. The performance-interpretability ranking is preserved under external validation on a demographically distinct adult cohort, providing evidence that performance-interpretability coupling is robust to population shift and supports the generalizability of the proposed framework beyond the pediatric training distribution. This work contributes a reproducible, end-to-end methodology that jointly optimizes performance and interpretability, offering practical guidance for selecting and deploying explainable deep learning models in clinical pneumonia screening. All code and data are publicly available at: https://github.com/SynergIA-Lab/pneumoniacnn