Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis

• URL: http://arxiv.org/abs/2310.03472v1
• Date: Thu, 5 Oct 2023 11:28:32 GMT
• Title: Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis
• Authors: Shunkai Shi, Yuqi Wang, Qihui Ye, Yanran Wang, Yiming Zhu, Muhammad Hassan, Aikaterini Melliou, Dongmei Yu
• Abstract summary: In Traditional Chinese Medicine, the tooth marks on the tongue serve as a crucial indicator for assessing qi (yang) deficiency. To address these problems, we propose a multi-task joint learning model named Ammonia-Net. Ammonia-Net performs semantic segmentation of tongue images to identify tongue and tooth marks.
• Score: 12.095100353695038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Traditional Chinese Medicine, the tooth marks on the tongue, stemming from prolonged dental pressure, serve as a crucial indicator for assessing qi (yang) deficiency, which is intrinsically linked to visceral health. Manual diagnosis of tooth-marked tongue solely relies on experience. Nonetheless, the diversity in shape, color, and type of tooth marks poses a challenge to diagnostic accuracy and consistency. To address these problems, herein we propose a multi-task joint learning model named Ammonia-Net. This model employs a convolutional neural network-based architecture, specifically designed for multi-class segmentation and classification of tongue images. Ammonia-Net performs semantic segmentation of tongue images to identify tongue and tooth marks. With the assistance of segmentation output, it classifies the images into the desired number of classes: healthy tongue, light tongue, moderate tongue, and severe tongue. As far as we know, this is the first attempt to apply the semantic segmentation results of tooth marks for tooth-marked tongue classification. To train Ammonia-Net, we collect 856 tongue images from 856 subjects. After a number of extensive experiments, the experimental results show that the proposed model achieves 99.06% accuracy in the two-class classification task of tooth-marked tongue identification and 80.02%. As for the segmentation task, mIoU for tongue and tooth marks amounts to 71.65%.

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