Diabetic Retinopathy Detection Through Deep Learning Techniques

Abstract

Diabetes mellitus frequently results in Diabetic Retinopathy (DR), which results in lesions on the retina that impact on vision. Blindness may result if it is not caught in time. Unfortunately, there is no cure for DR treatment merely preserves vision. Early diagnosis and treatment of DR can greatly lower the risk of visual loss. In contrast to computer-aided diagnosis technologies, the manual diagnosis of DR retina fundus images by ophthalmologists is costly, time-consuming, and prone to error. Deep learning has recently risen to prominence as one of the most popular methods for improving performance, particularly in the categorization and interpretation of medical images. Convolutional neural networks are more frequently utilized in medical picture analysis as a deep learning technique since they are extremely. The most cutting-edge ways for classifying and detecting DR colour fundus photos using deep learning techniques have been explored and examined for this paper. Additionally, the colour fundus retina DR datasets have been examined. There are also discussions on several complex subjects that demand further research.