RTS-Net: efficient and accurate network for real-time tongue segmentation

Lingli Zhang; Gongxin Shen; Yong Zhang

doi:10.1117/12.2671175

28 April 2023 RTS-Net: efficient and accurate network for real-time tongue segmentation

Lingli Zhang, Gongxin Shen, Yong Zhang

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126104E (2023) https://doi.org/10.1117/12.2671175
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

The tongue organ has been widely studied by researchers because of its special status in Traditional Chinese Medicine (TCM). Doctors can determine the health condition of patients by observing their tongues. With the development of Artificial Intelligence (AI) technology, many efficient models have been designed for automated diagnosis, especially for mobile devices. In this way, many people can use the tongue diagnostic system deployed to mobile devices for health management. However, the small memory and low imaging quality of mobile devices have been limiting the efficiency of diagnosis. To address these problems, we design the efficient and accurate network for real-time tongue segmentation (RTS-Net) on mobile devices. The RTS-Net consists of two parts: lightweight ghost encoder for accurate feature extractor with less parameters and efficient decoder to recover the tongue details. Specially, we take GhostNet as backbone and remove its last avgpool layer, fully connection layer and pointwise convolution. Then, the ASPP module is adopted to capture multi-scale features and abstract semantics. We also design an efficient and accurate decoder to recover the resolution of features as well as compensate for feature details. We collect tongue images taken from mobile devices on the web and build the corresponding dataset to test the effectiveness of our model for low-quality images taken by mobile devices. Overall, the dataset contains images of tongues taken in different backgrounds, various angles, diverse ages and non-uniform lighting. Extensive experiments are conducted on our mobile tongue dataset and the result shows that proposed method is lightweight and accurate for mobile tongue segmentation.

Citation Download Citation

Lingli Zhang, Gongxin Shen, and Yong Zhang "RTS-Net: efficient and accurate network for real-time tongue segmentation", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126104E (28 April 2023); https://doi.org/10.1117/12.2671175

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KEYWORDS

Tongue

Image segmentation

Mobile devices

Convolution

Feature extraction

Design and modelling

Semantics

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