FD-MagNet: A Filtered Deep MammographNet Framework with Chaotic Nutcracker Optimized Graph Attention for Enhanced Breast Cancer Classification

Breast cancer detection from mammographic images remains a complex task due to variations in tissue density, imaging inconsistencies, and therapy-induced phenotypic changes. Conventional methods often struggle with tissue-intensity ambiguity in BI-RADS 4 regions, leading to inaccurate lesion segmentation. To address these challenges, this study introduces the Filtered Deep MammographNet (FD-MagNet) , a novel unified deep learning framework for accurate breast cancer identification. Initially, Guided Trilateral Gabor Filter (GuGaF) enhances mammogram clarity by preserving edges and highlights directional patterns for early detection. During segmentation, alterations in dense breast tissue cause a tissue-intensity vagueness, to solve this, a 2D Masi-Kapur’s Bitterling Optimized Swin Transformer (2D MaBiS-T) is integrated, which combines entropy mapping, bio-inspired optimization, and hierarchical attention for precise tumor isolation. Further during extraction, the anatomical variations in mammographic views cause pectoralis muscle glooms, to overcome this a novel Mexican Axolotl Optimized ResNet150 (Mexi-Net) is employed to adaptively extract lesion features from sensitive regions. Finally, to resolve the loss caused by trastuzumab-induced phenotypic reversions, the Chaotic Nutcrackered Graph Rank Region-Based Attention Network (C-GRAN) is deployed to maintain diagnostic reliability through spatial awareness and targeted attention. Therefore, the FD-MagNet model achieves an accuracy of 99.60%, and precision of 99.7%, on DDSM dataset, demonstrating its robust diagnostic performance.