Detection of Abnormality in Human Body by Observing Change in Iris using Optimized Support Vector Machine Classifier

Abstract

In modern biomedical trend, it has been proved that the abnormalities in the system of human body can be diagnosed by the analysis on the change in anatomy of the human iris. Due to the fact that there is a direct relationship between human health and changes in the anatomy of the iris, this type of diagnosing methodology becomes trustworthy. The internal alteration in the properties of irises are used to diagnose the abnormalities. These internal alterations in iris is main focus of our iris recognition process.  From the literature survey, it is found that modern technology also fails in lot of cases to diagnose disease correctly. Thus the diagnosis is made using ancestors irido-diagnosis technique with modern technology. Research on computerized iridology had been done before. This paper presents a computerized iridology system for detecting abnormality conditions in the body. Which is designed with help of image processing through several stages such as pre-processing, feature extraction, classification using SVM based on Greywolf optimization algorithm. This system produces a better accuracy rate which is relatively higher than the existing noninvasive models.