A system for grading diabetic maculopathy severity level

Sharma, Purabi; Nirmala, S. R.; Sarma, Kandarpa Kumar

doi:10.1007/s13721-014-0049-y

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Candidate exudate regions were identified, and exudate/nonexudate decision on these candidate regions was done using various classifiers. Akram et al 59 used SVM, Reference 60 used GMM‐based classifier, Bayes classifier is used in Reference 61, and an ensemble of SVM and GMM was cited in Reference 62. A summary of these algorithms is given in Table 8.…”

Section: Retinal Image Analysis For Dr/dme Gradingmentioning

confidence: 99%

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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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Section: Retinal Image Analysis For Dr/dme Gradingmentioning

confidence: 99%

“…If there are no exudates the image will be graded as normal. Authors in References 58–63 used macula coordinates and macula to exudate distance in DME grading. The green component of color fundus images was used by algorithms, as this provide a strong contrast between exudates and the OD with a background.…”

Section: Retinal Image Analysis For Dr/dme Gradingmentioning

confidence: 99%