HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI

Accurate and understandable interpretation of mammograms is fundamental to dependable identification of breast cancer, which facilitates clinical trust and usefulness. The framework proposed in this paper is known as the ResNet50HierarchicalMultiScaleCross-Attention (HMC) Network, building upon ResNet50 by embedding Hierarchical Self Attention and Multi-Scale Cross Attention modules for enriched feature representation toward mammogram-based detection of breast cancer. Intra-layer self-attention together with inter-layer cross-attention may enable the model to learn local as well as global patterns and hence improve performance for classification tasks on the MIAS dataset. For explainability issues, Grad-CAMs, Grad-CAM++, and Score-CAM are included in the architecture. Such methods yield heatmaps whose more clinically relevant regions can be made explicit for automated diagnostics to become transparent and trusted. With a 5-fold cross-validation run, it attained a mean accuracy of 0.9972 (± 0.05), a mean precision value of 0.9851 (± 0.13), a recall of 0.9899 (± 0.19), F1-score that amounted to 0.9864 (± 0.07). The values for AUC-ROC and specificity were found to be quite high at 0.99(± 0.01) and about 0.9978(± 0.09), respectively, basically beating most baseline models like ResNet50, VGG19, VGG16, and ViT among others in performance metrics variance as indicated by the Friedman test (p-value=0.002 0.05). Between ResNet50 with Hierarchical Attention, ResNet50 with Multi-Scale Attention, and the proposed model using the Nemenyi post-hoc test, HMC-Attention clearly outperformed standard ResNet50; learning curves for stable convergence with limited overfitting provided evidence-based support that mammogram analysis is both accurate and transparent: the new baseline for automated diagnostics. This framework unites sturdy, profound implementation of deep learning with medical elucidation, setting paths toward trustworthy computer-supported diagnostic tools.