Explainable AI for breast cancer detection in mammography

Abstract

Breast cancer remains a significant global health concern among women. This research introduces an explainable AI-assisted breast cancer detection system aimed at improving both the accuracy and interpretability of mammogram-based diagnoses. The study utilizes high-quality mammographic datasets, CBIS-DDSM and the RSNA Screening Mammography dataset, to train and validate the models. The system uses two powerful deep learning models: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The InceptionResNetV2 CNN achieved an accuracy of 92%, while the ViT model reached 96% accuracy by effectively focusing on important regions in the mammogram images. To make the system more transparent, several Explainable AI (XAI) methods were applied, including Grad-CAM, SIDU, Attention Maps, and Ablation-CAM. Among these, SIDU provided the clearest and most accurate visual explanations, which are valuable for medical decisionmaking. To further improve the reliability and clinical value of the system, this study introduces a Dual-Stage Ensemble Diagnosis and Decision Fusion Framework. This approach combines the diagnostic strengths of both models to deliver a more confident and balanced final decision, supported by detailed visual explanations. The platform consist with a user-friendly web application that allows doctors and patients to easily upload mammogram images and receive AI-based predictions with clear and interpretable outputs. This research helps advance the development of trustworthy AI tools for breast cancer detection in real clinical settings.