Tongue Image Segmentation Algorithm Based on Dilated ADU-Net in Open Environment#br#
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doi:10.3969/j.issn.1006-2475.2024.04.009

Abstract

Abstract:
Abstract: Accurate tongue image segmentation is an important prerequisite for obtaining correct tongue diagnosis results. Aiming at the problem that traditional segmentation algorithms are difficult to accurately and stably segment tongue images under complex lighting conditions， an improved U-Net tongue image segmentation model （Dilated Attention & Dense U-Net， Dilated ADU-Net） combining dilated convolution dual attention mechanism and dense connection mechanism is constructed. Firstly， the backbone network is built based on the symmetric structure of U-Net network. Then， the downsampling module uses a cavity mixed attention module to make the network focus on tongue features， and the upsampling module uses a dense connection mechanism to fuse multi-layer feature information. Finally， the tongue image dataset in open environment is used to train the network to obtain the tongue image segmentation model. Experimental verification shows that compared with other advanced segmentation methods， the mean Intersection over Union （mIoU） of tongue image segmentation model constructed in this paper reaches 96.73% and the similarity coefficient Dice （DSC） reaches 98.08%， which has better segmentation performance and can realize accurate segmentation of tongue image in complex environments.

Key words: Key words: tongue segmentation, deep learning, attention mechanism, dense connection, open environment

CLC Number:

TP391

WANG Xin, XIN Guojiang, ZHANG Yang, ZHU Lei . Tongue Image Segmentation Algorithm Based on Dilated ADU-Net in Open Environment#br# #br#[J]. Computer and Modernization, 2024, 0(04): 48-54.

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