Tongue image segmentation and tongue color classification based on deep learning
- Author:
LIU Wei
1
;
CHEN Jinming
1
;
LIU Bo
1
;
HU Wei
1
;
WU Xingjin
1
;
ZHOU Hui
1
Author Information
1. School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
- Publication Type:Journal Article
- From:
Digital Chinese Medicine
2022;5(3):253-263
- CountryChina
- Language:English
-
Abstract:
Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis, including tongue image segmentation and tongue color classification, improving their diagnostic accuracy. Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results. A new dataset was constructed for tongue image segmentation. Tongue color was marked to build a classified dataset for network training. In this research, the Inception + Atrous Spatial Pyramid Pooling (ASPP) + UNet (IAUNet) method was proposed for tongue image segmentation, based on the existing UNet, Inception, and atrous convolution. Moreover, the Tongue Color Classification Net (TCCNet) was constructed with reference to ResNet, Inception, and Triple-Loss. Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification. IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+ for tongue segmentation. TCCNet for tongue color classification was compared with VGG16 and GoogLeNet. Results IAUNet can accurately segment the tongue from original images. The results showed that the Mean Intersection over Union (MIoU) of IAUNet reached 96.30%, and its Mean Pixel Accuracy (MPA), mean Average Precision (mAP), F1-Score, G-Score, and Area Under Curve (AUC) reached 97.86%, 99.18%, 96.71%, 96.82%, and 99.71%, respectively, suggesting IAUNet produced better segmentation than other methods, with fewer parameters. Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors. The experiment yielded ideal results, with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%, respectively. Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones. IAUNet can not only produce ideal tongue segmentation, but have better effects than those of PSPNet, SegNet, UNet, and DeepLabV3+, the traditional networks. As for tongue color classification, the proposed network, TCCNet, had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.
