Bibliographic Details
| Title: |
Semantic-Attention Enhanced DSC-Transformer for Lymph Node Ultrasound Classification and Remote Diagnostics |
| Authors: |
Ying Fu, Shi Tan, Michel Kadoch, Jinghua Zhong, Lifeng Guo, Yangan Zhang, Xiaohong Huang, Xueguang Yuan |
| Source: |
Bioengineering, Vol 12, Iss 2, p 190 (2025) |
| Publisher Information: |
MDPI AG, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Technology
LCC:Biology (General) |
| Subject Terms: |
deep learning, medical image analysis, ultrasound imaging, lymph node classification, semantic-attention enhanced DSC-transformer, Technology, Biology (General), QH301-705.5 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2306-5354 |
| Relation: |
https://www.mdpi.com/2306-5354/12/2/190; https://doaj.org/toc/2306-5354 |
| DOI: |
10.3390/bioengineering12020190 |
| Access URL: |
https://doaj.org/article/4e853dec7ac94e73bcdd530c039243e2 |
| Accession Number: |
edsdoj.4e853dec7ac94e73bcdd530c039243e2 |
| Database: |
Directory of Open Access Journals |
