Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis

Abstract

The rapid development of intelligent manufacturing provides strong support for the intelligent medical service ecosystem. Researchers are committed to building Wise Information Technology of 120 (WIT 120) for residents and medical personnel with the concept of simple smart medical care and through core technologies such as Internet of Things, Big Data Analytics, Artificial Intelligence, and microservice framework, to improve patient safety, medical quality, clinical efficiency, and operational benefits. Among them, how to use computers and deep learning technology to assist in the diagnosis of tongue images and realize intelligent tongue diagnosis has become a major trend. Tongue crack is an important feature of tongue states. Not only does change of tongue crack states reflect objectively and accurately changed circumstances of some typical diseases and TCM syndrome but also semantic segmentation of fissured tongue can combine the other features of tongue states to further improve tongue diagnosis systems’ identification accuracy. Although computer tongue diagnosis technology has made great progress, there are few studies on the fissured tongue, and most of them focus on the analysis of tongue coating and body. In this paper, we do systematic and in-depth researches and propose an improved U-Net network for image semantic segmentation of fissured tongue. By introducing the Global Convolution Network module into the encoder part of U-Net, it solves the problem that the encoder part is relatively simple and cannot extract relatively abstract high-level semantic features. Finally, the method is verified by experiments. The improved U-Net network has a better segmentation effect and higher segmentation accuracy for fissured tongue image dataset. It can be used to design a computer-aided tongue diagnosis system.

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

“…Due to its excellent performance, it was widely used in computer vision. Li et al 56 applied this model to the tongue crack segmentation, and they improved its encoder to extract relatively more abstract high-level semantic features. Similarly, Peng et al 57 also improved the U-Net framework and designed a lightweight model P-Net with the letter “P” structure to be suitable for remote tongue image segmentation.…”

“…Cai et al 67 changed the loss function for tongue segmentation and proposed a function that would decrease the intraclass distance and increase the interclass distance. To change the model to extract specific concerns, Li et al 56 introduced a global convolution network module to extract relatively abstract high-level semantic features, while Huang et al 54 constructed a receptive field block based on the receptive field theory that the region closer to the center of retinotopic maps is more important than others in distinguishing objects, making the model deal more with the blurred edge of the tongue body. Similarly, Peng et al 57 applied an attention module to intensify the attention to the boundary and suppress useless information.…”

“…In addition, some studies report special solutions for other tongue segmentation scenarios. Tang et al 11 reported a solution of tooth-marked recognition based on the segmented tongue, and Li et al 56 refined the U-Net model to especially segment the cracks on the tongue, the same as the reference. 57 The feature of sublingual vein had also been noticed and extracted, 10 , 68 Qiu et al’s 10 study added the cross-channel attention module to assign more weight to the target area, which shows an improved performance in their lightweight tongue diagnosis system on mobile devices, and another paper 68 proposed a two-stage segmentation method: a fully CNN network without downsampling to reduce the loss of spatial feature information effectively and another fully CNN network with proper dilated convolution to avoid the gridding issue.…”

“…The data set to supervise the tongue segmentation model is relatively small, which may cause generalization issues; Li et al 19 applied an iterative learning method to train the network on the good samples repeatedly judging by some specific filters. Others 54 , 56 used pretrained methods to alleviate the generalization problem. Some studies believe that tongue features must be extracted from the global image level to make segmentation, so the dilated convolution block was introduced.…”

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

“…Traditionally, restorative doctors will examine these color features relying on broad information. Be that as it may, subjectivity and equivocalness are frequently followed with their determinations result [3]. For eliminating these subjective elements, tongue color examinations are impartially inspected by their color features that give an original strategy to diagnosing infection, one that lessens the actual injury caused to individual (connected with another therapeutic examination).…”