Multi-View Mammogram Classification Using Transformer-GNN With Prototype Reasoning Optimized via HHO–SMA

This paper presents a novel deep learning framework for accurate and interpretable breast cancer detection using multi-view mammogram images. The architecture integrates a cross-attention transformer for global relational learning between craniocaudal (CC) and mediolateral oblique (MLO) views, and a graph neural network (GNN) to model intra-view region-level structural topology dependencies. A prototype reasoning module enables explainable classification by comparing extracted features with learned benign and malignant exemplars. To enhance model robustness and hyperparameter tuning, a Hybrid Harris Hawks Optimization–Slime Mould Algorithm (HHO–SMA) is employed to jointly optimize attention weights, prototype thresholds, and fusion parameters. The proposed method is evaluated on the INbreast dataset and demonstrates superior accuracy, sensitivity, and explainability compared to state-of-the-art convolutional neural network and transformer baselines. The incorporation of HHO–SMA significantly improves optimization convergence, ensuring high diagnostic reliability in clinical settings.