"Diabetes Prediction from Tongue Images Using Texture Features and RFC"

What is it about?

By looking at the picture of the tongue, tongue diagnosis is an essential diagnostic approach for assessing internal organ health. Traditional tongue diagnosis, on the other hand, has a restricted practical use because of its approximate, instinctive, and knowledge-based character. In addition to this, conventional tongue diagnosis is focused on identifying patterns instead of the relationship amid aberrant tongue growths and illnesses." To solve these issues, a new automated tongue examination approach has been developed. The Hough transform, edge detection, and gabor filter are used to extract three types of information from tongue images: shape, colour, and textural measurements. Color segmentation method and area of interest are used to distinguish the tongue from the rest of the picture during tongue image segmentation. Each tongue’s feature characteristics are plays a vital role. This system’s performance is shown by the outcomes of the experiments.

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Why is it important?

The automated system leverages advanced image processing techniques like the Hough transform, edge detection, and Gabor filters to analyze tongue images. This leads to a more precise and objective assessment of the tongue's shape, color, and texture, enhancing diagnostic accuracy.Unlike traditional methods that may suffer from subjectivity and variability between practitioners, this automated approach ensures consistency and repeatability in diagnoses.

Perspectives

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The automated tongue diagnosis system represents a significant advancement in traditional diagnostic methods, offering a more objective and precise assessment of internal organ health based on tongue appearance. This could lead to earlier detection of health issues and more personalized healthcare. By automating and quantifying the process of tongue diagnosis, this approach validates and modernizes a practice rooted in traditional medicine, potentially increasing its acceptance and use in contemporary medical settings.

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