Encoder-decoder network with RMP for tongue segmentation

Kusakunniran, Worapan; Borwarnginn, Punyanuch; Karnjanapreechakorn, Sarattha; Thongkanchorn, Kittikhun; Ritthipravat, Panrasee; Tuakta, Pimchanok; Benjapornlert, Paitoon

doi:10.1007/s11517-022-02761-3

Cited by 5 publications

(2 citation statements)

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“…TP is true positive; TN is true negative; FP is false positive; FN is false negative. p ii is the total number of pixels correctly identified as i for class i; p ij is the total number of pixels incorrectly identified as class j for class i ; k is the category: 27 – 29 MPA: Mean PA, that is, the average accuracy at the pixel level. Calculate the proportion of pixels correctly classified for each class to the total predicted pixels, and then divide the number of pixels correctly classified into all categories by the total number of pixels to obtain pixel-level accuracy.…”

Section: Experiments Resultsmentioning

confidence: 99%

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RTC_TongueNet: An improved tongue image segmentation model based on DeepLabV3

Tang,

Tan,

et al. 2024

DIGITAL HEALTH

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Objective Tongue segmentation as a basis for automated tongue recognition studies in Chinese medicine, which has defects such as network degradation and inability to obtain global features, which seriously affects the segmentation effect. This article proposes an improved model RTC_TongueNet based on DeepLabV3, which combines the improved residual structure and transformer and integrates the ECA (Efficient Channel Attention Module) attention mechanism of multiscale atrous convolution to improve the effect of tongue image segmentation. Methods In this paper, we improve the backbone network based on DeepLabV3 by incorporating the transformer structure and an improved residual structure. The residual module is divided into two structures and uses different residual structures under different conditions to speed up the frequency of shallow information mapping to deep network, which can more effectively extract the underlying features of tongue image; introduces ECA attention mechanism after concat operation in ASPP (Atrous Spatial Pyramid Pooling) structure to strengthen information interaction and fusion, effectively extract local and global features, and enable the model to focus more on difficult-to-separate areas such as tongue edge, to obtain better segmentation effect. Results The RTC_TongueNet network model was compared with FCN (Fully Convolutional Networks), UNet, LRASPP (Lite Reduced ASPP), and DeepLabV3 models on two datasets. On the two datasets, the MIOU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy) values of the classic model DeepLabV3 were higher than those of FCN, UNet, and LRASPP models, and the performance was better. Compared with the DeepLabV3 model, the RTC_TongueNet network model increased MIOU value by 0.9% and MPA value by 0.3% on the first dataset; MIOU increased by 1.0% and MPA increased by 1.1% on the second dataset. RTC_TongueNet model performed best on both datasets. Conclusion In this study, based on DeepLabV3, we apply the improved residual structure and transformer as a backbone to fully extract image features locally and globally. The ECA attention module is combined to enhance channel attention, strengthen useful information, and weaken the interference of useless information. RTC_TongueNet model can effectively segment tongue images. This study has practical application value and reference value for tongue image segmentation.

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