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http://dx.doi.org/10.9718/JBER.2021.42.5.201

Tongue Image Segmentation Using CNN and Various Image Augmentation Techniques  

Ahn, Ilkoo (Korean Medicine Data Division, Korea Institute of Oriental Medicine)
Bae, Kwang-Ho (Korean Medicine Data Division, Korea Institute of Oriental Medicine)
Lee, Siwoo (Korean Medicine Data Division, Korea Institute of Oriental Medicine)
Publication Information
Journal of Biomedical Engineering Research / v.42, no.5, 2021 , pp. 201-210 More about this Journal
Abstract
In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.
Keywords
Tongue segmentation; Tongue diagnosis; Image augmentation; Transfer learning; Convolutional neural network;
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Times Cited By KSCI : 3  (Citation Analysis)
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