Research On The Classification Of Tongue Fur Quality In TCM Based On The Innovative ч-Net Model And Its Visualization Of Tongue Fur Features

As computer-assisted medical treatment has attracted more and more attention and been applied,various computer-assisted medical treatment methods and methods have emerged one after another.However,more methods are only applied to Western medicine,and the involvement in the field of Chinese medicine is relatively There are few cases,and the combination of tongue diagnosis and computer technology in TCM diagnosis also needs to be enriched and developed.Tongue diagnosis,as a characteristic diagnosis method that is effective in traditional Chinese medicine,has been playing an important role in clinical practice since ancient times.The theoretical knowledge of tongue diagnosis is not complicated,but TCM doctors use theoretical models as the basic basis and image recognition as their basic skills in learning and practice,and through repeated training and practice to improve the accuracy of diagnosis,this requires a lot of clinical knowledge and experience.Generally speaking,the field of TCM tongue diagnosis can be diagnosed from multiple angles,including the quality of the tongue,the coating of the tongue,and the three parts under the tongue.The diagnosis of the tongue coating is one of the very important aspects.We built the ч-Net network model,based on the Grad-CAM method to classify and visualize TCM tongue fur texture,aiming to help TCM doctors speed up diagnosis efficiency and improve diagnosis accuracy through computer means.At the same time,our research shows that using segmented tongue images for model training can produce higher classification accuracy and better feature visualization.Specifically,we first realized the classification and feature visualization of tongue fur texture in the VGG13 network based on the Grad-CAM method.Since no satisfactory results were achieved,the idea of segmenting the tongue image was germinated,and the U-Net model was used to compare the original The tongue image has been segmented,achieving a segmentation accuracy of 98.24%.In the future,after obtaining new tongue image data,the tongue image can be perfectly segmented,which is convenient for us to carry out and optimize more subsequent experiments.Through observation,we can find that the U-Net model removes the right half of the expansion path,which is a VGG13 network model without a fully connected layer.Therefore,we changed the network structure of the U-Net model and repeatedly experimented to find the convolutional layer.After adding the best position of the fully connected layer,the ч-Net network model was constructed to realize the classification and feature visualization of the tongue fur texture after the tongue image was segmented.Compared with the VGG13 network model,the classification accuracy increased by nearly twelve percentage points,to achieve a better feature visualization effect.