HGCTransformer: Hybrid Gated CNN-Transformer for Breast Cancer Image Classification - Ye, Shengpei (International Journal of Computer Science and Information Technology, 2026)
Breast cancer is the most common malignant tumor among women, and histopathological images play a crucial role in the differential diagnosis of benign and malignant lesions. Although Convolutional Neural Networks (CNNs) and Transformers have been widely used in medical image classification, CNNs often struggle to capture global structural patterns, while Transformers may underperform in modeling fine-grained local features. To address this, we propose a Hybrid Gated CNN-Transformer (HGCTransformer) model for breast tumor histopathological image classification. The model introduces a Dual-Branch Convolution and Attention Residual Module (DBCARM) into the Transformer block to integrate local texture and global contextual information. Additionally, a Gated Multi-Scale Feed-forward Network (GMSFN) is designed to enhance the discrimination of multi-scale malignant features, such as nuclear atypia and architectural disarray. Experimental results on public breast histopathology dataset indicate that the proposed method achieves a classification accuracy of 99.76%, underscoring its potential for computer-aided diagnosis of breast cancer.