Smoker’s Tongue Recognition System based on Spectral and Texture Features using Visible Near-Infrared Imaging

Traditional Chinese Medicine theorizes a clear relationship between the visual characteristics of the tongue and the operational condition of the body's organs. The visual characteristics of the tongue can offer important indications for diagnosing diseases. Investigating tongue image processing methods for automated disease identification is a flourishing field of study in the modernization of Traditional Chinese Medicine. Although autonomous extraction of high-dimensional features is inherently more beneficial in deep learning than in conventional methods, its usefulness in medical image analysis, notably in tongue images, is restricted by the need for extensive training data. This limitation arises from the need for more labeled images. This paper demonstrated the automated diagnosis of tongue photos by analyzing digital images utilizing Image Processing techniques and using Machine Learning using major image-based features. The performance simulation and analysis of the suggested system are conducted using MATLAB Software.

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Smoker’s Tongue Recognition System based on Spectral and Texture Features using Visible Near-Infrared Imaging

Cited by 3 publications

References 11 publications

Improvement of Smoker Prediction System Based on Hyperspectral Image with Hybrid Deep Learning Model

Improvement of Smoker Prediction System Based on Hyperspectral Image with Hybrid Deep Learning Model

Pigmentation Prevalence Analysis of Smoker's Tongue Using Hyperspectral Imaging

Tongue Image Diagnosis System using Machine Learning with Hand-Crafted Features

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