A Comprehensive Study on the Automatic Identification of Diabetic Retinopathy

An eye condition known as diabetic retinopathy is caused by a higher blood sugar ratio. It usually doesn’t have any symptoms at the early stage, but in the later stage, the symptoms are visible, and the patient can find it difficult to see and can have irritation in the eye, which may result in blindness. Early discovery of it can lessen the spread of the disease in the eyes. Historically, diabetic retinopathy diagnosis was manual due to a lack of computerized technology. Manual detection-based methods have low accuracy, are time-consuming, and have side effects. Many methods have been put forward by researchers in order to discover diabetic retinopathy, such as machine learning, deep learning, image processing, data science, etc. These methods have used different datasets, including MESSIDOR, Kaggle, FUNDUS, EyePACS, DIRETDB1, macular optical coherence tomography, etc. In this paper, some of these recent proposed methods to detect diabetic retinopathy have been reviewed. The datasets that were employed by different techniques and the attained outcomes are presented in this paper. Also, the challenges and future plans of those methods are described. The comparative analysis of those methods will be helpful for a more thorough understanding of the detection of the disease.