Diabetic Retinopathy Diagnosis using Machine Learning Techniques
Diabetic Retinopathy Diagnosis using Machine Learning Techniques is a scholarly work, published in 2022. The main subjects of the publication include medicine, diabetic retinopathy, ophthalmology, artificial intelligence, health informatics, machine learning, and computer science. Diabetes mellitus affects vital organs of the human body including heart, kidneys but also eyes.The majority of patients have blood sugar fluctuations which may result in several ocular problems.Patients with history of diabetes mellitus for more than ten years may develop, indeed, eye-related diseases such as cataract, nerve tissue destruction, maculopathy and retinopathy.Visual deficit and visual impairment are, in fact, eminent problems among working-age adults.Diabetic retinopathy is a common retinal condition secondary to diabetes that if neglected, might result in serious eye damage.To avoid irreversible visual loss, effective identification, analysis and treatment are required at an early stage of diabetic retinopathy.Diabetic patients should actually have regular eye exams to confirm the non-appearance of diabetic retinopathy abnormalities.In the realm of medical discipline, computer vision and image treating methods play a major part in diabetes and recent ophthalmology.A Computer Aided Diagnosis's major purpose is to distinguish the initial indications of a certain disease from a medical picture that physicians can hardly notify with their naked eyes.Digital image treating is the processing of pictures in which the input is a video or image and the output is an image or a set of features connected to that picture.It most commonly relates to numerical picture processing.This review resumes identification methods of diabetic retinopathy based on the severity of its stages using different learning algorithms such as Support Vector Machine, multilayer perceptron, and Convolution neural networks.