An Explainable AI-Driven Deep Adaptive Learning Framework for Breast Cancer Classification Using Histopathology Images

A unified pipeline benchmarking three CNN backbones
under Plain and Transfer Learning regimes on BreakHis,
identifying fine-tuned DenseNet121 as the most accurate
configuration.
• Grad-CAM explainability integrated on the best model,
enabling inspection of the tissue regions driving each
prediction.
• An Adaptive Learning Engine that updates the deployed
model incrementally using a replay buffer and a safetyrollback check, avoiding both full retraining and catastrophic forgetting.
• An end-to-end incremental-update case study demonstrating the engine, including its versioning and audit-logging
mechanism, and a comparison against ex