Semantic-Attention Enhanced DSC-Transformer for Lymph Node Ultrasound Classification and Remote Diagnostics.

Abstract

This study presents a novel Semantic-Attention Enhanced Dynamic Swin Convolutional Block Attention Module(CBAM) Transformer (DSC-Transformer) for lymph node ultrasound image classification. The model integrates semantic feature extraction and multi-scale attention mechanisms with the Swin Transformer architecture, enabling efficient processing of diagnostically significant regions while suppressing noise. Key innovations include semantic-driven preprocessing for localized diagnostic focus, adaptive compression for bandwidth-limited scenarios, and multi-scale attention modules for capturing both global anatomical context and local texture details. The model's effectiveness is validated through comprehensive experiments on diverse datasets and Grad-Channel Attention Module (CAM) visualizations, demonstrating superior classification performance while maintaining high efficiency in remote diagnostic settings. This semantic-attention enhancement makes the DSC-Transformer particularly effective for telemedicine applications, representing a significant advancement in AI-driven medical image analysis with broad implications for telehealth deployment.