Analysis of diabetic retinopathy using naive bayes classifier technique - Sudha, V; Karthikeyan, C (International Journal of Engineering and Technology, 2018)
The vital issues of diabetes are Diabetic retinopathy (DR) and Retinal Vascular Disease which leads to the blindness. The DR disease may be detected by the early regular screening. and the automatic detection of this disease is a great solution and which is more reliable to identify the normality level in Fundus images (FI). The FI contains the texture discrimination capacity to differentiate the healthy images. The Data mining technique are used for identifying the retinal features of DR disease. The Data mining technique contains two stages. In first stage the features of DR disease extract from the Retinal Images (RI). The highlights for DR disease determination incorporate blood vessels, optic nerve, neural tissue, neuroretinal edge, optic plate size, thickness and change and which are removed by applying Data mining strategy. The result of the different information mining arrangement systems was looked at utilizing quick excavator apparatus. Gullible bayes and Support Vector Machine classifiers are utilized to anticipate the early discovery of eye disease diabetic retinopathy and observed that Naive bayes technique to be enhance the exactness of 89% precise.Â Â