YOLOv13-SwinTongue:使用带有Swin变压器的增强YOLOv13进行舌头涂层诊断
Xiangqiang Yang1, Jinchao Hao1, Yonggang Wang1
1Henan Communications Engineering Co., Ltd., Zhengzhou 450044, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究引入了一个AI模型来分析舌头涂层,这是中国传统医学的关键诊断工具. 增强的YOLOv13模型提高了舌头诊断的准确性,朝着客观和标准化的评估迈进.
科学领域:
- 综合医学是一个整体医学.
- 医疗保健中的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 在传统中医药 (TCM) 中,舌头涂层分析对于评估健康至关重要.
- 目前的视觉检查方法是主观的,导致诊断不一致.
- 需要客观和标准化的方法来进行TCM舌头诊断.
研究的目的:
- 开发一种用于舌头涂层分析的智能诊断模型.
- 为了增强YOLOv13对象检测算法,以改进舌头特征识别.
- 建立一个客观和标准化的TCM舌头诊断的基础.
主要方法:
- 开发了一个增强的YOLOv13模型,集成了一个swin变压器.
- 混合架构捕捉了全球上下文和本地纹理特征.
- 该模型经过训练和评估,以识别细粒度的舌头涂层.
主要成果:
- 与原始YOLOv13.13相比,增强的YOLOv13模型表现出优越的性能.
- 在细粒度特征提取方面观察到显著改善.
- 实现了对舌头涂层特征的增强边界定位精度.
结论:
- 拟议的智能模型为TCM中客观的舌头诊断提供了一种可靠的方法.
- 这一进步支持TCM诊断实践的标准化和智能进步.
- 这种人工智能驱动的方法解决了TCM主观视觉检查的局限性.
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