摘要
目的:解决目前齿痕舌的识别方法上存在效率低、准确率不高的问题。方法:本研究提出了一种基于二级分类器的齿痕舌分类模型。首先准备齿痕舌和非齿痕舌两类舌图像样本,利用深度学习分割模型对舌体进行分割;再利用凸包算法提取齿痕舌图像中的每个齿痕的几何形状特征,训练齿痕识别模型;然后利用该模型预测舌体凸包凹缺陷的齿痕候选区域,构建齿痕舌识别的特征向量,基于随机森林建立齿痕舌分类模型;最后进行模型分类测试和结果评估。结果:采用该方法得到的总体分类准确率达到93%。结论:该方法取得了较好的齿痕舌分类效果,为齿痕舌的识别研究提供了一种新的思路,对舌诊客观化和现代化具有一定借鉴意义和实用价值。
Objective:To solve the problem of the current method for identifying teeth-printed tongue has low efficiency and low accuracy.Methods:Model was established by a classification of teeth-printed tongue based on two-level classifier.First,prepare two kinds of tongue image samples of teeth-printed tongue and non-dented tongue,and use the deep learning segmentation model to segment the tongue;use the convex hull algorithm to extract the geometric characteristics of each teeth-print in the teethprinted tongue image.Train the teeth-print recognition model;then use the model to predict the teeth-print candidate area of the tongue convex hull defect,construct the feature vector of the teeth-printed tongue recognition,build the teeth-printed tongue classification model based on the random forest;finally carry out the model classification test and result evaluation.Results:The overall classification accuracy rate obtained by this method reached 93%.Conclusion:This method has achieved a good classification effect of indented tongue,provides a new idea for the identification of indented tongue,and has certain reference significance and practical value for the objectification and modernization of tongue diagnosis.
作者
颜建军
李东旭
郭睿
燕海霞
王忆勤
YAN Jian-jun;LI Dong-xu;GUO Rui;YAN Hai-xia;WANG Yi-qin(Institute of Intelligent Perception and Diagnosis,School of Mechanical and Power Engineering.East China University of Science and Technology,Shanghai 200237,China;Laboratory of Information Access and Synthesis of Traditional Chinese Medicine Four Diagnosis,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China)
出处
《中华中医药杂志》
CAS
CSCD
北大核心
2022年第4期2181-2185,共5页
China Journal of Traditional Chinese Medicine and Pharmacy
基金
国家自然科学基金面上项目(No.81673880)。
关键词
二级分类器
舌诊客观化
齿痕
机器学习
深度学习
凸包算法
随机森林
Two-level classifier
Objectification of tongue diagnosis
Teeth-print
Machine learning
Deep learning
Convex hull algorithm
Random forest
