EXPLAINABLE DEEP LEARNING FOR BREAST CANCER DETECTION: BRIDGING ACCURACY AND INTERPRETABILITY

Abstract

Breast cancer is one of the leading causes of cancer-related mortality among women worldwide, making early and accurate diagnosis essential for improving patient survival and treatment outcomes. Conventional breast cancer diagnosis primarily relies on mammography, ultrasound, magnetic resonance imaging (MRI), and histopathological examination interpreted by experienced radiologists. Although Deep Learning (DL) has achieved remarkable success in automated breast cancer detection, many existing models operate as "black-box" systems, limiting clinicians' trust due to the lack of interpretability and transparency in their predictions. Explainable Artificial Intelligence (XAI) has emerged as a promising solution for addressing this challenge by providing visual and interpretable explanations for deep learning decisions. This paper proposes an explainable deep learning framework that integrates advanced Convolutional Neural Networks (CNNs) with Explainable AI techniques for accurate and transparent breast cancer detection. The proposed framework incorporates image preprocessing, deep feature extraction, tumor classification, Grad-CAM visualization, attention mechanisms, and interpretable decision support to assist clinicians in understanding model predictions. Comparative evaluation demonstrates that the proposed approach significantly improves diagnostic accuracy, precision, recall, F1-score, and model transparency while maintaining high computational efficiency. The generated visual explanations enable radiologists to verify detected tumor regions and increase confidence in AI-assisted diagnosis. The proposed framework contributes to trustworthy medical AI by bridging the gap between predictive performance and clinical interpretability, supporting intelligent breast cancer diagnosis, precision medicine, and reliable clinical decision-making.