A MULTSTAGE APPROACH FOR EXUDATES DETECTION IN FUNDUS IMAGES USING TEXTURE FEATURES WITH K-NN CLASSIFIER
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Abstract
Human eye is one of the common organs affected by diabetes. Diabetic retinopathy is a retinal disease that is characterized by vascular changes causing swellings of capillaries known as Microaneurysms and exudates. Exudates are an accumulation of lipid due to vascular leakage in the vitreous humor. The focus of the work presented in this paper is to detect exudates which are an important symptom for Diabetic Retinopathy. The proposed algorithm consolidates morphological operations for blood vessel removal, segmentation and optic disk removal. The proposed technique extracts texture features and uses k-NN classifier to segregate diseased and healthy images. It demonstrates promising outcomes with higher PPV, Specificity, Sensitivity values. It is observed from experimentation that the average values of PPV is100% Specificity yields 100% and Sensitivity yielded 98.2% for DIARETDB0 database.
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