Natural Environment Tongue Image Segmentation Method Based on Improved Labv3+ Model

doi:10.3969/j.issn.1006-2475.2025.10.006

Abstract

Abstract:
Abstract: Tongue image segmentation in natural environments poses great challenges due to factors such as lighting and background interference. This paper proposes a DeepLabv3-MAC model based on improved DeepLabv3+ algorithm for segmenting tongue images captured in natural settings. Firstly, the backbone network of the DeepLabv3+ model is replaced with a MobileNetv2 network to reduce model complexity. Secondly, an asymmetric convolutional module is adopted to enhance the convolutional kernel skeleton of convolutional neural network, thereby improving the utilization of convolutional information. Lastly, the introduction of the CBAM attention mechanism not only focuses on the importance of parameters in space and channels， but also enhances the segmentation accuracy of the model. Experimental results demonstrate that, compared to classical tongue image segmentation algorithms, the proposed DeepLabv3-MAC model exhibits superior segmentation performance. Additionally, the model significantly reduces the number of parameters, enabling faster segmentation of tongue images in natural environments and facilitating future deployment on mobile devices.

Key words: Key words: tongue image segmentation, DeepLabv3+, DeepLabv3-MAC, asymmetric convolution module, CBAM attention mechanism

CLC Number:

中图分类号：TP391

LIU Rongcheng1, XIN Guojiang1, ZHANG Yang1, ZHU Lei2. Natural Environment Tongue Image Segmentation Method Based on Improved Labv3+ Model[J]. Computer and Modernization, 2025, 0(10): 32-36.

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Natural Environment Tongue Image Segmentation Method Based on Improved Labv3+ Model

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