A SURVEY REPORT ON DETECTION OF DIABETICS IN RETINAL IMAGES USING MACHINE LEARNING ALGORITHMS
DOI:
https://doi.org/10.61841/46r0nq98Keywords:
Diabetic Retinopathy, Pre-processing, algorithms, retinal images, Data sets, performance, classificationAbstract
Diabetic retinopathy is a pervasive eye disease in diabetic patients, and it is the most widely known cause of diabetic visual blindness. Diabetic retinopathy requires early detection to prevent patients from losing their eyesight. This paper focuses on decisions regarding the existence of disease through the use of machine learning algorithms. The main reason is to immediately identify the degree of diabetic retinopathy that is nonproliferative in any retinal image. ⠀ In this case, the initial eye treatment stage isolates necessary characteristics of blood vessels (microaneurysms, hardened exudates) to determine the retinopathy of any retinal image using the support vector machine and other algorithms such as Random Forest, Naïve Bayes, K Neighbor, and Decision tree are used for classification . This paper thus focuses on the performance of disease detection using machine learning algorithms and also focuses on previous approaches for the development of efficient algorithms.
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