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...

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Principais autores:	A. Rajasekhar Yadav, Vaegae Naveen Kumar
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	IEEE 2026-01-01
סדרה:	IEEE Access
נושאים:	Cross-attention transformer graph neural network (GNN) hybrid Harris Hawks optimization–slime Mould algorithm (HHO–SMA) mammogram
גישה מקוונת:	https://ieeexplore.ieee.org/document/11450372/
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