A Review on Detection of Diabetic Retinopathy using Deep Learning and Transfer Learning based Strategies

Diabetic Retinopathy (DR) is considered to be one of the most widely observed and a complex variation of diabetes and stands as a leading cause of blindness globally. The occurrence of DR causes impairment in the retinal blood vessels and leads to unusual growth of blood arteries in the eye. Manual examinations and analysis suggests that the prevalence of DR has been enormously growing at an exponential rate and has already registered for more than 160 million cases worldwide. On the other hand, its diagnostic screening is not only challenging, but also computationally expensive at the same time. Due to the highlighting importance of its early diagnosis in terms of treatment, multiple concepts to DR detection have been used in the past few years. However, research in recent times has resulted in the fact that deep learning based CNN structures and Transfer Learning based MedNets have been popularly used in DR detection, due to its superior performance in the medical domain. As a result of such advancements in Deep Learning methodologies, this article proposes a review on automated approaches used to detect diabetic retinopathy using image processing and disease classification techniques. The review is further preceded with a comprehensive analysis on training a model with an already pre-trained network whose primary goal is to generate useful information and provide it to diabetic researchers, medical practitioners and patients