Diabetic Retinopathy is the major problem in Diabetic affected people, as it
causes blindness to the people who are affected by this particular disease. Recently
many people are being affected by Diabetes due to changes in food habits and the
quality of food people take. Early and continuous testing of the eye in regular intervals
leads to the identification of this disease by which the blindness problem can be
overcome, but due to lack of availability of resources like optholomists and the
machinery for monitoring the eyes the early detection is not that easy. The early
machines and methods adopted will not provide good results in any cases because of
many constraints, As the technology is advancing, with the help of neural networks and
Edge Devices regular monitoring the diabetic people can be done easily and effectively
in any part of the globe with much ease. At present image processing techniques are
being used. In this study, we propose Deep Learning algorithms that process the date
very accurately in very less time because of which we could good results in
performance evaluation. In this study we have also proposed Edge Device which are
end term devices, where these devices perform analytics on images of the eye and
predict the condition of the eye and also it gives the information of stages of Diabetic
Retinopathy, we can also store the data which is processed by edge device on cloud
servers via wifi, From the cloud server the concerned people can obtain the records of
that person who is affected with Diabetic Retinopathy.
Keywords: Convolution Neural Networks(CNN), Diabetic Retinopathy, Edge
Device, Neural Networks.
