Automated detection of diabetic retinopathy using SVM

Carrera, Enrique V.; González, Andrés; Carrera, Ricardo

doi:10.1109/intercon.2017.8079692

Cited by 154 publications

(62 citation statements)

References 6 publications

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“…A. Pre-processing Pre-processing is an important step in process of detecting DR. Most of the researchers convert retinal fundus image into green channel [1][2][3], [5][6][7][8], [10][11][12][13][14] as a contrast of MAs and HMs are high in this channel. Contrast limited adaptive histogram equalization (CLAHE) is applied to enhance the image and then median filter to remove noise [4, 6, 7 and 10].…”

Section: Methodsmentioning

confidence: 99%

“…Fuzzy C-Means clustering and morphological operations are used to detect blood vessels [10]. The Retinal fundus image is converted into a CMY color image after that isolate Magenta channel [11]. On that channel morphological operations and thresholding is applied.…”

Section: Methodsmentioning

confidence: 99%

“…The Morphological operation followed by watershed segmentation is used to segment optic disc in [8]. Create a binary image of Difference between a green channel and cyan channel of retinal fundus image after that dilation and Hough transform are applied to create optic disc mark [11].…”

Section: ) Optic Disc Detectionmentioning

confidence: 99%

“…The First operation used top-hat transformation and illumination correction for background exclusion and other operation uses blood vessels removal, segmentation and logical '&' operation. Convert retinal fundus image into a CMY color model and then extract magenta component [11] to detect EXs. On magenta component applied thresholding base on a standard derivation of a magenta component to convert an image into binary image and then remove optic disc portion from a binary image.…”

Section: ) Exs Detectionmentioning

confidence: 99%

“…Pre-processing techniques followed by extraction of features like blood vessels, MAs, optic disc, EXs and then applied SVM classification [10]. Enrique et al [11] applied same flow chart as [10] but used different methods for extraction and used two classifiers SVM and decision tree. After applying pre-processing technique feature extraction, feature selection using Haar wavelets and principal component analysis is done and then applied classification techniques like OneR and BPNN [12].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Diabetic Retinopathy: A Survey

Panchal¹

2018

IJRASET

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Diabetic Retinopathy (DR) is a severe condition for the diabetic patient in their eye ailment. This issue can be detected from the retinal fundus image. Microaneurysms (MAs), Hemorrhages (HMs) and Exudates (EXs) are three features of nonproliferative diabetic retinopathy. Microaneurysms are the earlier stage of DR. There are many methods or techniques proposed till date for detection of the disease in the eye from the retinal fundus image. This paper aims to survey some techniques to detect blood vessels, optic disc exudates (EXs), microaneurysms (MAs) and Hemorrhages (HMs) and then classified using different classifier.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: ) Optic Disc Detectionmentioning

confidence: 99%

Section: ) Exs Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Diabetic Retinopathy: A Survey

Panchal¹

2018

IJRASET

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A comprehensive review on automated systems for severity grading of diabetic retinopathy and macular edema

Mathews

Anzar

2021

Int J Imaging Syst Tech

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Diabetes mellitus is a major medical concern worldwide. Long-term diabetes can affect the retina of the eye and lead to diabetic retinopathy (DR) and diabetic macular edema (DME). Proper screening and consultation with an ophthalmologist are necessary to prevent avoidable vision loss. As DR and DME have become more prevalent, automated screening is essential to provide costeffective and rapid solutions with reduced human resources requirements. This paper aims to provide a comprehensive review of the literature on computer-aided diagnosis of DR and DME. We identified the studies on automated five-class grading of DR according to International Clinical Diabetic Retinopathy severity scale and three class grading of diabetic maculopathy, using fundus images. A systematic search on research repositories was conducted, and relevant studies were scrutinized and included in the review. The studies were reported in nearly 100 different journals. We have reviewed the studies in all aspects including datasets, preprocessing, non-deep learning, and deep learning-based algorithms, and evaluation metrics. Significant contributions in developing automated tools for DR/DME grading are highlighted. We have identified and discussed research gaps and challenges. This will help researchers to get an updated summary of work done in the area. Deep learning-based algorithms have outperformed the traditional algorithms in the domain. Despite their promising performance, these algorithms reveal the potential for significant improvements to become a reliable tool in clinical settings.

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