Multi-Backbone Ensemble Learning for Robust Breast Cancer Classification on the Mini-DDSM Mammography Dataset

This study presents a rigorous patient-grouped evaluation framework for breast cancer classification on the Mini-DDSM dataset, while identifying and addressing cross-folder patient-identifier collisions that can distort patient grouping and fold composition. It systematically evaluates EfficientNet-B4 and ConvNeXtV2-Tiny using test-time augmentation, threshold analysis, and Grad-CAM explainability, providing a reproducible reference framework for robust mammography classification.