Dual-Branch Attention Network for Multi-Resolution Breast Cancer Histology Classification

Breast cancer histopathological diagnosis is vital for patient prognosis, yet existing automated approaches struggle to reconcile local cellular detail with global tissue context across varying magnifications. To bridge this gap, we present a dual-branch framework that couples a CBAM-enhanced ResNet101 for fine-grained feature recalibration with a hierarchical Swin Transformer that models long-range dependencies. The two branches are aligned through a lightweight multi-resolution fusion module that unifies features from 40 ×, 100 ×, 200 ×, and 400 × scans into a complementary representation. Evaluated on the BreakHis dataset, the proposed model achieves up to 97.8% accuracy and consistently surpasses state-of-the-art CNN and pure-Transformer baselines across all magnification levels. These findings advance multi-scale digital pathology, offering a scalable tool that can enhance diagnostic accuracy, accelerate pathology workflows, and ultimately contribute to improved cancer care and public health.