Multi-Class Chest Disease Classification from X-Rays Using PCA-Enhanced Vision Transformers and Multi-Method Explainability

Main Research Contributions:
– Dimensionality reduction through PCA. To curb overfitting on uneven and heterogeneous data, we place Principal Component Analysis (PCA) inside the preprocessing stage, which lowers the number of features and the computational load while holding on to the information that separates the classes.
– Locality-aware attention transferred to radiography. We adopt the MLLKSA block of Luong et al., developed for breast histopathology, and fine-tune it on chest X-rays (CXRs). The contribution here is the transfer and its empirical assessment, not the mechanism itself.
– A transformer and CNN ensemble. To bring together their differing inductive biases, we join the fine-tuned Vision Transformer with convolutional networks in an ensemble setup. The transformer branch handles long-range global structure while the convolutional branch is stronger on local detail, and together they lift robustness and generalization.
– The ability to explain everything in detail. In order to guarantee that model decisions are clear, clinically reliable, and robust to shortcut learning, we incorporate a multi-method interpretability pipeline that uses gradient-based and model-agnostic visualization tools.