  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%.
