Diabetic retinopathy grading using bayesian approach

Abstract

Diabetes-related microvascular alterations in the retina create newlinediabetic retinopathy, the leading cause of permanent vision loss worldwide. newlineMicroaneurysms (MAs), hemorrhages (HEs), soft exudates (SEs), and hard newlineexudates (HEs) are all examples of retinal lesions that may be seen on retinal newlinefundus imaging and help identify DR (EXs). A universally accepted strategy to newlineprevent blindness is routine DR screening followed by prompt evaluation and newlinetreatment. However, an increasing worldwide diabetic population at risk for newlineDR makes it difficult to deploy DR screening programmes. Retinal image newlineanalysis with computer-aided illness detection might be a viable method for newlinedoing such a large-scale screening. Biomedical researchers now have a way to newlineaccomplish this aim, thanks to developments in computational power and newlinemethods for analysing images. To aid in the creation, validation, and newlinecomparison of DR lesion segmentation methods, the research community need newlineboth raw pictures and exact pixel or image level expert annotations. Accurately newlinesegmenting lesions aids in diagnosing disease severity and serves as a guide for newlinetapping disease development in subsequent operations. A much-desired newlinesolution to the current situation is automatic DR detection. Ophthalmologists newlinemay save both time and money with the use of automatic DR detection in their newlinediagnostic processes. The research presented in this thesis was conducted newlineprimarily with the end goal of fostering the creation of cutting-edge methods newlinefor automated DR screening. We suggest three modules for rapid diagnosis and newlinecategorization of diabetic retinopathy in this study. newlineSmall and big blood vessels in the fundus picture may be newlinedistinguished with the use of the first module s dual-scale analysis. Candidate newlinelesion discovery FP areas are minimized with the use of length filtering and newlinecommunity entropy thresholding. Classification decisions are made using newlineKNN, SVM, and HRF classifiers. newline