Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/458432
Title: Machine learning and deep learning based strategies for diagnosis of diabetic maculopathy from sdoct retinal scans
Researcher: Padmasini N
Guide(s): Umamaheswari R
Keywords: Diabetic Maculopathy
Diabetic Retinopathy
Retinal Layers
University: Anna University
Completed Date: 2021
Abstract: One of the major complications in human eyes due to Diabetes is newlineDiabetic Retinopathy (DR). Diabetic retinopathy occurs when the blood or newlineother fluids leaks from tiny blood vessels of the light-sensitive retinal tissues newlineand accumulate. Due to this the retinal layers gets swollen, resulting in cloudy newlineor blurred vision. It can cause loss of vision if it is left undiagnosed and newlineuntreated. newlineDR includes mainly three stages background retinopathy, newlinepreproliferative stage and proliferative stage. Diabetic Maculopathy (DM) is a newlinecondition of DR and is the main cause of vision loss. In DM, the macula newlineregion, center portion of retina gets affected. DM can occur in any stage of newlineDR, but it likely increases as DR worsens. The chances of occurrence of DM newlineare high in persons with uncontrolled Type 1 or Type 2 diabetes for more than newline10 years duration. newlineRetina is a ten layered structure and the breakdown of blood retinal newlinebarrier leads to Diabetic Macular Edema (DME), which is the cause of DM. newlineInitially, edema starts occurring in the outer nuclear layer or outer plexiform newlinelayer and hence the entire retinal layer thickness increases. In the advanced newlineDM stage, DME may cause several structural changes in the retinal layers. newlineThe crucial task is automated detection of DM in the early stage and newlineidentification of the structural pattern involved in the latter stage. Therefore newlineanalysis of retinal layers is the foremost task in the prevention of vision loss. newline
Pagination: xviii,148p.
URI: http://hdl.handle.net/10603/458432
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File24.1 kBAdobe PDFView/Open
02_prelim_pages.pdf3.34 MBAdobe PDFView/Open
03_contents.pdf186.33 kBAdobe PDFView/Open
04_abstracts.pdf185.75 kBAdobe PDFView/Open
05_chapter1.pdf1 MBAdobe PDFView/Open
06_chapter2.pdf355.35 kBAdobe PDFView/Open
07_chapter3.pdf187.33 kBAdobe PDFView/Open
08_chapter4.pdf948.63 kBAdobe PDFView/Open
09_chapter5.pdf2 MBAdobe PDFView/Open
10_chapter6.pdf1.7 MBAdobe PDFView/Open
11_chapter7.pdf979.56 kBAdobe PDFView/Open
12_annexures.pdf406.87 kBAdobe PDFView/Open
80_recommendation.pdf163.11 kBAdobe PDFView/Open
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