IRIS IMAGE BASED DIABETIC PREDICTION USING FUZZY CLUSTERING ALGORITHM AND SVM CLASSIFICATION MODEL
Iris image analysis for clinical diagnosis is one of the most efficient non – invasive diagnosis methods for determining health status of organs Correct and timely diagnosis is a critical, yet essential requirement of medical science. The attempt is being made to explore the area of diagnosis from different perspectives .The approach used is a combination of ancestor’s technology Irido-diagnosis with modern technology. Irido-diagnosis is an alternative branch of medical science, which can be used for diagnostic purposes the different algorithms are developed for image quality assessment, segmentation of iris, iris normalization and clinical feature classification for clinical diagnosis. In this paper analysis a simple and non – invasive method to detect diabetic in body and iris recognition is not only mainly for biometric identification but it can also be used as a mean to detect diabetic or maybe diagnose any diseases as iridology claimed it is supposed to be. For clinical feature analysis, enhancement is essential for extraction of deep layer features. For feature extraction various image enhancement methods like arithmetic operation, histogram equalization, and adaptive histogram equalization have been applied.
Download Full Abstract: https://iridology-research.com/pdf/IRIS-IMAGE-BASED-DIABETIC-PREDICTION.pdf