Constitution identification model in traditional Chinese medicine based on multiple features

基于多特征的中医体质辨识模型研究

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.

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。