Explainable Mammogram Analysis with EfficientNetV2 and Grad-CAM++ for Robust Cancer Diagnosis.

Background: Breast cancer remains a leading cause of mortality among women worldwide, underscoring the need for timely and accurate detection. Conventional mammographic diagnosis, while widely used, is limited by subjectivity and variability in interpretation. Recent advances in deep learning (DL) have improved automated detection; however, the black-box nature of these models raises concerns regarding clinical trust and interpretability. Methods: To address this, we propose an explainable DL framework for breast cancer classification using mammographic images. The approach employs contrast limited adaptive histogram equalization (CLAHE)-based preprocessing to enhance lesion contrast, EfficientNetV2 for feature extraction, and the convolutional block attention module (CBAM) to refine salient features. For interpretability, gradient-weighted class activation mapping++ (Grad-CAM++) is used to highlight discriminative regions influencing predictions. Results: The framework is evaluated on three publicly available datasets-MIAS, DDSM, and InBreast-individually and under cross-dataset settings. Results demonstrate superior performance over existing methods, achieving classification accuracies of 99.85%, 99.40%, and 99.70% on MIAS, DDSM, and InBreast, respectively, with corresponding F1-scores of 99.75%, 99.10%, and 99.55%. Confusion matrix analysis confirms excellent sensitivity for malignant cases, and time complexity assessments show reduced training and inference overhead compared to conventional deep models. Conclusions: The framework thus provides a robust and interpretable solution for mammogram-based breast cancer screening.