实用医学杂志2026，Vol.42Issue(3)：395-405,11.DOI:10.3969/j.issn.1006-5725.2026.03.006
深度学习构建舌象分类系统在银屑病中医证型识别中的应用
Application of a deep learning-based tongue image classification system in TCM syndrome identification of psoriasis
摘要
Abstract
Objective To develop a deep learning-based tongue image classification system for psoriasis to improve the objective differentiation between the patterns of Spleen Deficiency with Dampness Retention(SDDR)and Blood Stasis(BS).Methods A total of 981 tongue images from psoriasis patients diagnosed with SDDR or BS were collected.An improved U-Net model,featuring a ResNet-34 encoder,bilinear interpolation upsampling,and optimized skip connections,was employed for automatic tongue region segmentation.Macenko color normalization and the Albumentations library were applied for data augmentation to mitigate variances from imaging devices and lighting conditions.A two-stage framework was constructed:the first stage precisely extracted the tongue body region,while the second stage utilized a Hybrid Model integrating EfficientNet-B3 and Swin-Tiny architectures for pattern classification.A cross-modal multi-head attention mechanism was introduced to fuse local textural and global structural features.Results The improved U-Net achieved superior performance in tongue seg-mentation,with a Dice coefficient of 0.98 and an IoU of 0.89,significantly outperforming the original U-Net(Dice 0.85).For pattern classification,the Hybrid Model demonstrated the best overall performance,achieving a 5-fold cross-validation mean accuracy of 0.9816 and a mean AUC of 0.9993.The F1-score was significantly higher than those of individual models.Macenko normalization contributed to an 8.3% increase in F1-score.The inference time per image was 38 ms on an A10 GPU,meeting the requirement for clinical real-time application.Conclusion The constructed two-stage tongue image classification model effectively and accurately distinguishes between SDDR and BS patterns in psoriasis,significantly enhancing the objectivity of tongue diagnosis.It provides a reliable tool for pattern differentiation in Traditional Chinese Medicine and shows promising potential for clinical application.关键词
银屑病/脾虚湿阻证/血瘀证/中医舌诊/深度学习Key words
psoriasis/spleen deficiency with dampness obstruction syndrome/blood stasis syndrome/traditional chinese medicine tongue diagnosis/deep learning分类
医药卫生引用本文复制引用
蔡淑娴,朱东宁,徐皓玮,黄嘉淇,李逍宇,李卓豪,刘秀峰,闫玉红..深度学习构建舌象分类系统在银屑病中医证型识别中的应用[J].实用医学杂志,2026,42(3):395-405,11.基金项目
省部共建中医湿证国家重点实验室重点项目(编号:SZ2021ZZ37) （编号:SZ2021ZZ37）
广州市科学技术局市校企联合资助项目(编号:2024A03J0727) （编号:2024A03J0727）
广东省中医院慢病管理专项课题(编号:YN2024MB007) （编号:YN2024MB007）
广州中医药大学"筑峰造尖"行动计划(编号:GZY2025GB0417) （编号:GZY2025GB0417）
第七批全国老中医药专家学术经验继承工作项目(编号:国中医药人教函[2022]76号) （编号:国中医药人教函[2022]76号）
