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ReserarchID
2JZH1
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
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Diabetic retinopathy is an ophthalmic inflammation caused by diabetes, which ends in visual defacement if not diagnosed earlier, and that has two types, namely Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). NPDR features are present in the earliest stage, and systematic detection of these features can improve the diagnosis of the disease severity formerly. Several detection methods exist previously. Still, there is performance lack on large datasets. The objective of this study is detecting NPDR features from diabetic retinopathy fundus images of large datasets with performance level. The study has investigated different fuzzy-based systems and to execute the objective; the GK_FCM approach was proposed, which integrates Gaussian Kernel function in conventional FCM. The execution has four phases. Initially, the input image undergoes preprocessing using green channel extraction, median filter to enhance the image quality and background removal is performed with extended minima transform technique, mathematical arithmetic operation and pixel replacement method to remove the outlier called Fovea (FV).
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