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Diabetes is a chronic disease in which a person's body either does not respond to insulin released by the pancreas or does not create enough insulin. Diabetics are at a higher risk of developing a variety of eye disorders over time. Diabetic retinopathy(DR) is one such disease, which refers to the bursting of blood vessels in the retina as Diabetes increases. It is regarded to be the major cause of blindness since it arises without presenting any symptoms in its early stages. It is critical to identify and diagnose DR patients as soon as possible in order to receive the appropriate medical care. People have recognised the promising future of AI and healthcare integration due to the growth of artificial intelligence(AI) and the gradual commencement of AI research in the medical field in recent years. For example, the hot deep learning discipline has showed tremendous promise in disease diagnosis and drug response prediction. The accuracy of medical disease prediction has steadily improved, as has the performance in all aspects, from the initial logistic regression model to the machine learning model and eventually to the deep learning model of today. Several significant studies on the detection of diabetic eye disease have previously been published. This review looks at three aspects of diabetic eye disease detection: i) Image Processing Techniques, ii) Machine Learning Approaches. iii) Methods of Deep Learning in detection of Diabetic Retinopathy. In addition, performance measures and deep learning's future direction are addressed, with the goal of providing vital insight into research communities, healthcare professionals, and diabetic patients. |
