Healthcare systems worldwide face increasing pressure to deliver accurate, affordable, and scalable diagnostic services while maintaining long-term sustainability. Chest X-ray screening is considered one of the most cost-effective methods for detecting lung disease. However, many deep learning approaches are computationally intensive and difficult to interpret, which limits their adoption in high-throughput, resource-constrained clinical settings. This study proposes a hybrid CNN–tree framework for automated lung disease classification from chest X-ray images, which targets COVID-19, pneumonia, tuberculosis, lung cancer, and normal cases. To ensure robustness and generalization, four publicly available chest X-ray datasets from different sources are merged into a unified five-class dataset, which introduces realistic variations in imaging conditions and patient populations. A ResNet-18 model is fine-tuned to extract domain-specific deep feature representations. Feature dimensionality and redundancy are reduced using Principal Component Analysis, while class imbalance is addressed through the Synthetic Minority Over-sampling Technique. The resulting compact feature vectors are used to train interpretable tree-based classifiers, which include Decision Tree, Random Forest, and XGBoost. Experiments conducted using five-fold stratified cross-validation demonstrate substantial and consistent performance gains. When trained on fine-tuned and preprocessed deep features, all evaluated tree-based classifiers achieve weighted F1-scores between 0.977 and 0.982 using five-fold cross-validation, with a significant reduction in inter-class confusion. In addition, the proposed framework maintains low per-sample inference latency, which supports energy-efficient and scalable deployment. These results indicate that combining deep feature learning with interpretable tree-based models provides a practical and reliable solution for sustainable chest X-ray screening in real-world clinical environments.

Local label manipulation can threaten federated medical-image classification. We evaluated Pneumonia-to-Normal poisoning in a client-local feature-federated ensemble using 20,795 chest X-rays from three sources. Three of 10 simulated clients with differing class distributions changed all Pneumonia classifier-training labels to Normal. Across 10 seeds, matched attack and zero-poison runs kept perceptually linked images within the same partition. The vulnerable configuration used first-in-first-out retention, equal weighting, and no tuning. The protected configuration used local-validation retention, validation weighting, and class-score tuning. Their mean attack-minus-control increases in Pneumonia-to-Normal error were 12.12 and 0.79 percentage points, respectively, with 95% confidence intervals of 7.51–16.72 and 0.14–1.43. The paired reduction was 11.33 points, with a 95% confidence interval of 6.86–15.80. Without retraining, tuning alone and retention with weighting alone left increases of 0.91 and 4.11 points, respectively. Complete server-validation Pneumonia-to-Normal contamination in protected control and attacked runs produced an increase of 21.41 points when tuning averaged F1 over classes present in validation labels or predictions, versus 17.90 over all four classes. Respective 95% confidence intervals were 13.17–29.65 and 9.53–26.27. This scorer sensitivity limits contamination-effect interpretation. Inference remains exploratory and conditional on the selected attack direction. Simulated clients, unavailable patient identifiers, and lack of source-held-out testing limit generalization.