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http://dx.doi.org/10.9708/jksci.2021.26.09.037

Tongue Segmentation Using the Receptive Field Diversification of U-net  

Li, Yu-Jie (School of Intelligent Manufacturing, Weifang University of Science and Technology, Dept. of Computer and Software Engineering, Wonkwang University)
Jung, Sung-Tae (Dept. of Computer and Software Engineering, Wonkwang University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.9, 2021 , pp. 37-47 More about this Journal
Abstract
In this paper, we propose a new deep learning model for tongue segmentation with improved accuracy compared to the existing model by diversifying the receptive field in the U-net. Methods such as parallel convolution, dilated convolution, and constant channel increase were used to diversify the receptive field. For the proposed deep learning model, a tongue region segmentation experiment was performed on two test datasets. The training image and the test image are similar in TestSet1 and they are not in TestSet2. Experimental results show that segmentation performance improved as the receptive field was diversified. The mIoU value of the proposed method was 98.14% for TestSet1 and 91.90% for TestSet2 which was higher than the result of existing models such as U-net, DeepTongue, and TongueNet.
Keywords
Tongue; Segmentation; Deep Learning; U-net; Receptive Field;
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