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基于多特征的中医体质辨识模型研究

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目的 构建精准的中医体质辨识模型,以更好地指导临床诊断和治疗方案的制定,提高医疗效率和治疗效果.方法 首先,本文使用中医全身望诊数据采集设备采集健康人群的全身站立图像,并通过中医体质量表(CCMQ)确定中医体质,构建带有体质标签的图像数据集.其次,通过热压阀(HSV)颜色空间提取面色特征,并改进局部二值模式(LBP)算法提取形体特征,同时通过双分支深度网络提取全身站立图像的深度特征.最后,通过随机森林(RF)算法对多特征进行学习,建立中医体质辨识模型.选择准确率、精确率和F1值作为评估模型性能的三个指标.结果 实验结果表明,基于多特征的中医体质辨识模型的准确率、精确率和F1 值分别为 0.842、0.868和 0.790.相比单一的面色特征、形体特征、深度特征辨识模型,包含多特征的辨识模型准确率分别提高了 0.105、0.105和 0.079,精确率分别提高了 0.164、0.164 和 0.211,F1 值分别提高了 0.071、0.071和 0.084.结论 研究结果证实了本文所提出的融合面色特征、形体特征和深度特征的多特征在中医体质辨识的可行性,能够进一步完善中医体质辨识客观化和智能化.
Constitution identification model in traditional Chinese medicine based on multiple features
Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions,thereby offering optimized guidance for clinical diagnosis and treatment plan-ning,and ultimately enhancing medical efficiency and treatment outcomes.Methods First,TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people,from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ),and a dataset encompassing labelled constitutions was constructed.Second,heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition,a dual-branch deep network was employed to collect deep features from the full-body standing images.Last,the random forest(RF)algorithm was utilized to learn the extracted multifea-tures,which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy,precision,and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy,precision,and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842,0.868,and 0.790,respectively.In comparison with the identification models that encompass a single feature,either a single facial complexion feature,a body shape feature,or deep features,the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105,0.105,and 0.079,the precision increased by 0.164,0.164,and 0.211,and the F1 score rose by 0.071,0.071,and 0.084,respectively.Conclusion The research findings affirmed the viability of the proposed model,which incor-porated multifeatures,including the facial complexion feature,the body shape feature,and the deep feature.In addition,by employing the proposed model,the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

Traditional Chinese medicine(TCM)Constitution identificationDeep featureFacial complexion featureBody shape featureMultiple features

徐安迎、王天舒、杨涛、韩笑、张晓雨、王子琰、张琪、李筱、商洪才、胡孔法

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南京中医药大学人工智能与信息技术学院,江苏南京 210023,中国

中国中医科学院中医临床基础医学研究所,北京 100700,中国

北京中医药大学中医内科学教育部重点实验室,北京 100700,中国

北京中医药大学东方医院,北京 100078,中国

江苏省中医药防治肿瘤协同创新中心,江苏南京 210023,中国

南京中医药大学江苏省智慧中医药健康服务工程研究中心,江苏南京 210023,中国

南京中医药大学江苏重大健康风险管理与中医药防控政策研究中心,江苏南京 210023,中国

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中医 体质辨识 深度学习 面色特征 形体特征 多特征

2024

数字中医药(英文)
湖南中医药大学,中华中医药学会

数字中医药(英文)

ISSN:2096-479X
年,卷(期):2024.7(2)