A novel system that uses machine learning to predict tongue disease.
By Pooja Toshniwal PahariaReviewed by Lily Ramsey, LLMAug 15 2024 In a recent study published in Technologies, researchers devised a novel system that uses machine learning to predict tongue disease.
Automated tongue color analysis systems have demonstrated high accuracy in identifying healthy and ill individuals and diagnosing various disorders. Artificial intelligence has tremendously advanced in capturing, analyzing, and categorizing tongue images. The color models were as follows: the Human Visual System , the red, green, and blue system , luminance separation from chrominance , and lightness with green-red and blue-yellow axes .
Researchers used laptops with the MATLAB App Designer program installed and webcams with 1,920 x 1,080 pixels resolution to extract tongue color and features. Image analysis included segmenting the central region of the tongue image and eliminating the mustache, beard, lips, and teeth for analysis. The 0.99 Jaccard index with 0.01 zero-one losses, 0.92 G-score, 0.01 Hamming loss, 1.0 Cohen's kappa, 0.4 MCC, and 0.98 Fowlkes-Mallow index suggested nearly perfect positive correlations, suggesting that XGBoost is highly reliable and effective for tongue analysis. XGBoost ranked first in precision, accuracy, F1 score, recall, and MCC.
Tongue Anemia Asthma Covid-19 Diagnostic Healthcare Hospital Imaging Machine Learning Medicine Research
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