Explainable AI Techniques for Interpretable Breast Cancer Classification

Breast cancer is still a major health risk for women all over the world, and thus finding it early is very important for the patient's survival. Digital Breast Tomosynthesis (DBT) offers enhanced imaging capabilities relative to conventional mammography; yet, its quasi-3D characteristics provide distinct interpretability issues, often rendering deep learning models as black boxes. This work tackles the issue of transparency by testing three Explainable Artificial Intelligence (XAI) methods: Gradient-weighted Class Activation Mapping (Grad-CAM), Score-CAM, and Local Interpretable Model-Agnostic Explanations (LIME). The ResNet-50 architecture was utilized to analyse a dataset of 396 DICOM images that had been pre-processed in a unique way, including colour-mapping and balancing. The study used Insertion and Deletion Area Under the Curve (AUC) to carefully quantify how reliable the visual explanations were, in addition to usual criteria like accuracy, which achieved 94%. It was shown that LIME and Score-CAM generated attention maps that were dispersed or inconsistent, whereas Grad-CAM always showed lesion-specific areas with great accuracy. Grad-CAM was the best method for analysing DBT findings, since it had the highest Insertion AUC of 0.9078. These results provide radiologists with a way to trust and check automated diagnoses, which closes the gap between AI that works well and AI that is reliable in the clinic.