Abstract
Abstract: Diabetic Retinopathy is a diabetes problem that affects the eye. Injury to the blood vessels of the light sensitive tissue inside the rear of the eye (retina) is that the most reason for diabetic retinopathy. To begin with, Diabetic Retinopathy may have no symptoms or just cause minor vision problems. It has the potential to lead to blindness. Machine learning approaches can be used for the early detection of Diabetic Retinopathy. This paper proposes an automated Diabetic Retinopathy detection system that can detect the presence of Diabetic Retinopathy from retinal images. This work uses ResNet50 for the detection and classification of Diabetic Retinopathy. ResNet50 is a type of neural network used as a backbone for many computer-vision tasks. This paper proposes a machine learning model which is developed using ResNet50, then the model will be deployed as a user-friendly web application where the user can upload the retinal images as input to the system then system will detect the presence of Diabetic Retinopathy and classifies it into the stage or class which the particular image belongs to. Keywords: Diabetic Retinopathy, ResNet50, Proliferative diabetic retinopathy, non-proliferative diabetic retinopathy.
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