SpinalDenseXtNet: An Explainable Hybrid Deep Learning Framework for Mammographic Breast Cancer Classification

Abstract

Breast cancer is one of the primary causes of cancer-related deaths among women. Using mammography images for initial and precise findings is therefore vital. However, mammogram analysis is hindered by low contrast, noise, and high class similarity. This research presents an explainable and noise-resistant breast cancer recognition system utilizing a hybrid deep learning (DL) model called SpinalDenseXtNet. First, extended tuned adaptive frost filtering (Ext-AFF) is used to enhance mammographic images from the Mammographic Image Analysis Society (MIAS) and the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) datasets. PSPNet is employed for lesion segmentation. The segmented ROIs are then classified using the proposed SpinalDenseXtNet. Finally, Grad-CAM is used to visualize diagnostically relevant areas and enhance model interpretability. The proposed method’s performance is assessed using standard clinical metrics, thereby corroborating the efficiency and reliability of the proposed framework. The suggested model achieved 99.80% precision, 99.63% recall, 99.65% F1-score, 99.82% mean average precision (mAP), 98.48% Dice coefficient, and 99.71% accuracy.