  Diabetic Retinopathy DR is a popular disease for many people as a result of
age or the diabetic, as a result, it can cause blindness. therefore, diagnosis
of this disease especially in the early time can prevent its effect for a lot
of patients. To achieve this diagnosis, eye retina must be examined
continuously. Therefore, computer-aided tools can be used in the field based on
computer vision techniques. Different works have been performed using various
machine learning techniques. Convolutional Neural Network is one of the promise
methods, so it was for Diabetic Retinopathy detection in this paper. Also, the
proposed work contains visual enhancement in the pre-processing phase, then the
CNN model is trained to be able for recognition and classification phase, to
diagnosis the healthy and unhealthy retina image. Three public dataset
DiaretDB0, DiaretDB1 and DrimDB were used in practical testing. The
implementation of this work based on Matlab- R2019a, deep learning toolbox and
deep network designer to design the architecture of the convolutional neural
network and train it. The results were evaluated to different metrics; accuracy
is one of them. The best accuracy that was achieved: for DiaretDB0 is 100%,
DiaretDB1 is 99.495% and DrimDB is 97.55%.
