Extraction Zoning feature to diabetic retinopathic detection models

Abstract

The health sector is one area that has been applying various computer technologies. To diagnose a patient's illness was already done with computers. One is to diagnose diabetic Retinopathic disease that can happen to anyone. Diabetic Retinopathy, which is one of the com-plications caused by diabetes. Symptoms shown from this disease is mikroneurisma, hemorrhages, excudets and neovascularos. The de-tection of the disease is done by looking at the information on the retinal image and can then be classified according to severity. This research aims to develop a method that can be used utuk classify Diabetic Retinopathy. The process of classification is based fiture-fiture the retinal image obtained by the extraction process using extraction methods Zoning. The process is then performed to classify the Bayes Method and the results obtained Diabetic Retinopahty classification. The results of this study yield maximum accuracy 65%.