Research On Intelligent Diagnosis Method Of Yin Deficiency Syndrome Based On Multimodal Information Fusion

Yin deficiency is a basic symptom in TCM,which refers to a series of symptoms caused by the deficiency of yin essence or fluid in the human body,mainly manifesting as irritability and heat in the five hearts,difficulty in food intake,dry mouth and throat,and soreness and weakness in the waist and knees.Traditional Chinese medicine practitioners obtain information about the patient’s signs and symptoms through the four diagnosis methods of "looking,smelling,asking and cutting",which are highly subjective and easily influenced by the practitioner’s personal experience and perception,so different doctors may arrive at different diagnoses for the same case.Artificial intelligence technology can effectively solve the problem of subjectivity and inefficiency of traditional TCM diagnosis methods.However,most current diagnostic models for yin-deficiency evidence use single modal data for diagnosis,which cannot obtain comprehensive disease diagnosis information.Therefore,this paper constructs an intelligent diagnostic model based on multimodal information fusion for yin-deficiency evidence using machine learning and deep learning techniques,aiming to improve the diagnostic accuracy of yin-deficiency evidence.In the first part of this paper,three diagnostic models based on unimodal information are proposed for the diagnosis of yin-deficiency evidence in TCM.In terms of scale data processing,an improved algorithm based on random forest(RF-CGFS)is proposed in this paper,which effectively improves the classification accuracy.In terms of image data processing,this paper designs a Res Net50-SAM tongue image classification model and a Res Net50 combined with voting mechanism for eye image diagnosis,which improves the image classification accuracy.In the second part of this paper,an adaptive multimodal information fusion algorithm is designed to fuse the results of three unimodal diagnostic models at decision level.The fusion algorithm achieves highprecision diagnosis of yin-deficiency evidence by assigning a greater fusion weight to the model with high accuracy.At the same time,this article has developed an intelligent diagnostic system software for Yin deficiency syndrome,making the methods and algorithms proposed in this article truly have practical application value.