Diabetic Retinopathy Detection using Deep Learning.

Long-term diabetes is associated with diabetic retinopathy (DR), an eye disorder. DR is the main reason behind blindness in persons of working age globally, affecting an estimated 93 million individuals. If DR is detected early enough, it may be possible to treat or slow the course of vision impairment. However, this can be challenging because the condition usually doesn't manifest until it's too late for effective treatment. To diagnose DR based on the occurrence of lesions connected to the disease's vascular abnormalities, an ophthalmologist or other qualified medical practitioner must view and assess digital colour fundus photographs of the retina. This arduous manual process is being employed today. The automated DR screening technique, which will quicken the detection and decision-making processes, will aid in the management or control of DR advancement. Using machine learning models like CNN (Convolutional Neural Network), VGG-16, and VGG-19, this study provides an automated classification system that evaluates fundus images with various lighting and fields of view and provides a diabetic retinopathy severity score (DR). The dataset contains five criteria, with values ranging from 0 to 4, where 0 denotes an absence of DR and 4 denotes proliferative DR. [ABSTRACT FROM AUTHOR]