摘要
目的中医舌象诊察特征繁多,包括舌色、苔色、舌形态和舌动态等,约有30多种。目前普遍采用独立的方式分别进行分析,未能充分利用不同诊察特征之间的关联关系,同时也大大增加了分析系统的整体实现复杂度。方法为此,该文提出了一种基于关系网络的中医舌色苔色协同分类方法,通过关系网络来学习舌色苔色两个标签之间的非线性关联关系,可以在一个框架下同时实现舌色和苔色分类两个任务。首先,构建了一种双分支轻型卷积神经网络,通过设计低、高层特征融合模块,结合坐标注意力机制,能够以较低的模型复杂度实现较高的分类精度;其次,设计了一个舌色、苔色标签关系非线性学习网络,通过学习,挖掘出两者之间的关联关系信息,并将这一信息作为补充,用于舌色、苔色的协同分类。结果在两个自建的中医舌象分类数据集上的实验结果中,舌色分类的准确率分别达到了95.17%和93.67%,苔色分类的准确率则分别达到了91.11%和90.53%。结论提出的方法可以将整体复杂度降低50%左右,同时还可以提升两个分类任务的精度。
Objective There are so many tongue diagnosis features that need to be analyzed in Traditional Chinese Medicine(TCM),including tongue color,coating color,tongue morphology,and tongue dynamics,with about 30 different categories.Different tongue diagnosis features are often analyzed by individual methods,which fails to fully utilize the correlation between diagnostic features and greatly increases the overall implementation complexity of the TCM analysis system.Methods Therefore,in this paper,a TCM tongue color and coating color collaborative classification method based on relation network has been proposed.Firstly,a dual branch lightweight convolutional neural network has been constructed.By designing low and high layer feature fusion modules and combining Coordinate Attention mechanism,high classification accuracy can be achieved with lower model complexity.Secondly,a network for the tongue color and coating color relationship leaning has been designed to model the correlation between the two labels.By the network,the nonlinear information can be learned,which is used as a supplement for collaborative classification.Results The experimental results on two self-established TCM tongue diagnosis feature analysis datasets show that,the accuracy of tongue color classification reaches 95.17%and 93.67%respectively,while that of coating color classification reaches 91.11%and 90.53%respectively.Conclusion The proposed collaborative classification method can improve the classification accuracy of the two tasks,but simultaneously,the overall model complexity is reduced by almost half.
作者
王恩慈
卓力
李艳萍
王欣
杨洋
魏玮
WANG Enci;ZHUO Li;LI Yanping;WANG Xin;YANG Yang;WEI Wei(School of Information Science and Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent Systems,Beijing University of Technology,Beijing 100124,China;Wangjing Hospital,China Academy of Chinese Medical Sciences,Beijing 100102,China)
出处
《世界科学技术-中医药现代化》
北大核心
2025年第5期1207-1218,共12页
Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology
基金
国家自然科学基金委员会重点项目(62431001):多模态舌象信息感知与智能分析,负责人:卓力
国家中医药管理局中医药创新团队及人才支持计划项目(ZYYCXTD-C-202210):中医药防治消化道癌前疾病传承与创新团队,负责人:魏玮。
关键词
中医舌诊
舌色苔色协同分类
深度学习
关系网络
特征融合
TCM tongue diagnosis
Collaborative classification of tongue color and coating color
Deep learning
Relational network
Feature fusion
