  Many people are affected by diabetes around the world. This disease may have
type 1 and 2. Diabetes brings with it several complications including diabetic
retinopathy, which is a disease that if not treated correctly can lead to
irreversible damage in the patient's vision. The earlier it is detected, the
better the chances that the patient will not lose vision. Methods of automating
manual procedures are currently in evidence and the diagnostic process for
retinopathy is manual with the physician analyzing the patient's retina on the
monitor. The practice of image recognition can aid this detection by
recognizing Diabetic Retinopathy patterns and comparing it with the patient's
retina in diagnosis. This method can also assist in the act of telemedicine, in
which people without access to the exam can benefit from the diagnosis provided
by the application. The application development took place through
convolutional neural networks, which do digital image processing analyzing each
image pixel. The use of VGG-16 as a pre-trained model to the application basis
was very useful and the final model accuracy was 82%.
