  Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes
mellitus damaging the blood vessel network present in the retina. This may
endanger the subjects' vision if they have diabetes. It can take some time to
perform a DR diagnosis using color fundus pictures because experienced
clinicians are required to identify the tumors in the imagery used to identify
the illness. Automated detection of the DR can be an extremely challenging
task. Convolutional Neural Networks (CNN) are also highly effective at
classifying images when applied in the present situation, particularly compared
to the handmade and functionality methods employed. In order to guarantee high
results, the researchers also suggested a cutting-edge CNN model that might
determine the characteristics of the fundus images. The features of the CNN
output were employed in various classifiers of machine learning for the
proposed system. This model was later evaluated using different forms of deep
learning methods and Visual Geometry Group (VGG) networks). It was done by
employing the images from a generic KAGGLE dataset. Here, the River Formation
Dynamics (RFD) algorithm proposed along with the FUNDNET to detect retinal
fundus images has been employed. The investigation's findings demonstrated that
the approach performed better than alternative approaches.
