BCECNN: an explainable deep ensemble architecture for accurate diagnosis of breast cancer

The BCECNN model presents a promising solution for improving both the accuracy and interpretability of breast cancer diagnostic systems. Unlike many previous studies that rely on single architectures or large datasets, BCECNN leverages the strengths of an ensemble of CNN models and performs robustly even with limited data. It integrates advanced XAI techniques-such as Grad-CAM and LIME-to provide visual justifications for model decisions, enhancing clinical interpretability. Moreover, the model was validated using AISSLab dataset, designed to reflect real-world diagnostic challenges. This combination of EL, interpretability, and robust performance on small yet clinically relevant data positions BCECNN as a novel and reliable decision support tool for AI-assisted breast cancer diagnostics.