Application of Machine Learning Techniques for Tongue Diagnosis in Ayurveda
The analysis of tongue image is a very crucial approach in order to evaluate human health in Ayurveda medication. As a result of the modification in tongue color might counsel physical or mental disorders. Many tongue color quantification strategies for tongue diagnosis are published by many researchers in Chinese medication. However, reliable tongue color analysis algorithms are limited for Ayurveda medicine. The main objective of this paper is to apply advanced techniques and algorithms of digital image processing and Machine learning to quantify and verify clinical knowledge of tongue color identification by characterizing variations in tongue features. Tongue images are captured from good quality camera with sufficient lighting conditions, and collected about 60 tongue images. Active contour segmentation algorithm based on edge information is applied on input image to segment the tongue area, and then apply clustering technique using K-means. Clustering approach is applied to separate tongue-body and coating area. The result of segmenting tongue body and coating is very good in CIELAB color space.
Keywords: K-means clustering, CIELAB, tongue diagnosis, adaptive segmentation, tongue images
Cite this Article Sumanth N.S., N. Satish Kumar, Harshvardhan Tiwari, Balaji S., Prabhanjan S., Meenakshi Malhothra, Pallavi C.V. Application of Machine Learning Techniques for Tongue Diagnosis in Ayurveda. Journal of Advancements in Robotics. 2020; 7(1): 8–14p.
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