首页|基于深度学习ConvNeXt模型的冠心病痰湿证舌诊信息分类辨识

基于深度学习ConvNeXt模型的冠心病痰湿证舌诊信息分类辨识

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目的 通过卷积神经网络对冠心病痰湿证的舌象进行分类识别,提高冠心病痰湿证舌象识别准确率.方法 选取2020年10月至2022年8月在内蒙古自治区乌兰浩特市妇幼保健院、陕西中医药大学第二附属医院等地采集到的200例冠心病患者,其中痰湿证组、非痰湿证组各100例,运用ConvNeXt模型、朴素贝叶斯网络、K近邻模型、决策树算法、支持向量机模型进行舌象分类辨识.结果 不同模型的舌象分类平均准确度均在50％以上,ConvNeXt模型的平均准确度最高为89.44％;ConvNeXt模型验证集中痰湿证和非痰湿证2个类别的平均准确度、精确率、F1值和召回率均接近90％.结论 使用ConvNeXt模型进行舌象分类识别,能够较为准确地从舌诊上区分冠心病痰湿证与非痰湿证,客观化的人工智能识别技术,可以辅助冠心病痰湿证的临床诊断,有助于中医舌诊客观化研究的发展.
Classification and identification of tongue diagnosis information of phlegm-dampness syndrome of coronary heart disease based on deep learning ConvNeXt model
Objective To classify and identify the tongue image of phlegm-dampness syndrome of coronary heart disease by convolutional neural network,so as to improve the recognition accuracy of tongue image of coronary heart disease.Methods From October 2020 to August 2022,200 pa-tients with coronary heart disease were collected from Ulanhot Maternal and Child Health Hospital in Inner Mongolia Autonomous Region,and the Second Affiliated Hospital of Shaanxi University of Chinese Medicine and so on,including 100 patients in the phlegm-dampness syndrome group and 100 patients in the non-phlegm-dampness syndrome group.ConvNeXt model,naive Bayesian network,K nearest neighbor,decision tree algo-rithm,and support vector machine model were used to classify and identify tongue image.Results The average accuracy of tongue image classifica-tion of different models was above 50％,and the average accuracy of ConvNeXt model was 89.44％.ConvNeXt model verified that the average accu-racy,accuracy,Fl value,and recall rate of the two categories of concentrated phlegm-dampness syndrome and non-phlegm-dampness syndrome were close to 90％.Conclusion Tongue image classification and recognition using ConvNeXt model can more accurately distinguish phlegm-damp-ness syndrome of coronary heart disease from non-phlegm-dampness syndrome in tongue diagnosis.Objectified artificial intelligence recognition technology can assist clinical diagnosis of phlegm-dampness syndrome of coronary heart disease and contribute to the development of objectified research on tongue diagnosis of traditional Chinese medicine.

Classification of tongue imagesDeep learningConvolutional neural networkPhlegm-dampness syndromeCoronary heart disease

莫国凤、王烨、常娜娜、刘佳、汪南玥

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中国中医科学院医学实验中心,北京 100700

中国中医科学院中医药健康产业研究所,江西南昌 330000

舌象分类 深度学习 卷积神经网络 痰湿证 冠心病

北京市科技新星计划项目(交叉合作课题)中国中医科学院科技创新工程项目中国中医科学院医学实验中心中央级公益性科研院所基本科研业务费专项资金项目

C12021A05207JBGS2021008

2024

10.20047/j.issn1673-7210.2024.16.05

中国医药导报
中国医学科学院

中国医药导报

CSTPCD
影响因子：1.759
ISSN：1673-7210
年,卷(期)：2024.21(16)