Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/525809
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dc.coverage.spatialDiabetic retinopathy grading using bayesian approach
dc.date.accessioned2023-11-15T11:22:57Z-
dc.date.available2023-11-15T11:22:57Z-
dc.identifier.urihttp://hdl.handle.net/10603/525809-
dc.description.abstractDiabetes-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
dc.format.extentxvi,146p.
dc.languageEnglish
dc.relationp.133-145
dc.rightsuniversity
dc.titleDiabetic retinopathy grading using bayesian approach
dc.title.alternative
dc.creator.researcherNancy W
dc.subject.keywordDiabetics
dc.subject.keywordHemorrhages
dc.subject.keywordMicroaneurysms
dc.description.note
dc.contributor.guideCeline Kavida A
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensions21cm.
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

Files in This Item:
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01_title.pdfAttached File3.43 MBAdobe PDFView/Open
02_prelim pages.pdf1.63 MBAdobe PDFView/Open
03_contents.pdf3.43 MBAdobe PDFView/Open
04_abstracts.pdf3.43 MBAdobe PDFView/Open
05_chapter1.pdf3.43 MBAdobe PDFView/Open
06_chapter2.pdf3.44 MBAdobe PDFView/Open
07_chapter3.pdf3.44 MBAdobe PDFView/Open
08_chapter4.pdf3.44 MBAdobe PDFView/Open
09_chapter5.pdf3.43 MBAdobe PDFView/Open
10_annexures.pdf250.55 kBAdobe PDFView/Open
80_recommendation.pdf68.9 kBAdobe PDFView/Open


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