MS-EGT-Net: a multi-scale enhanced graph-transformer network for diabetic foot ulcer classification

Diabetic foot ulcers (DFUs) are a serious complication of diabetes, and accurate, timely classification is crucial for supporting clinical decision-making. However, many existing approaches struggle to adequately capture the multi-scale and spatially heterogeneous characteristics of DFU morphology, which often limits their ability to integrate fine grained tissue details with broader contextual patterns. To address these limitations, we propose the Multi-Scale Enhanced Graph-Transformer Network (MS-EGT-Net), which uses a dual-scale feature extraction framework to process both high-resolution and downsampled region-of-interest (ROI) patches with a shared backbone. This design maintains semantic consistency while simultaneously capturing microscopic textures and global wound structures. In addition, a selective token refinement mechanism prunes less informative regions based on attention weight analysis, thereby retaining diagnostically relevant areas and enriching them with contextual information. The model further incorporates an adaptive graph encoding strategy that combines semantic affinity with spatial proximity to represent histopathological relationships and local structural coherence, and a bidirectional cross-scale attention module that promotes reciprocal integration between local and global features to form a more comprehensive diagnostic representation. Experimental results demonstrate that MS-EGT-Net consistently outperforms state-of-the-art methods across multiple evaluation metrics, indicating that it provides an effective solution for DFU classification with strong potential for clinical application.