Abstract—Early detection of the diagnosis of some diseases in the retina of the eye can improve the chances of cure and also prevent blindness. In this study, a Convolutional Neural Network (CNN) with different architectures (Scratch Model, GoogleNet, VGG, ResNet, MobileNet and DenseNet) was created to make a comparison between them and find the one with the best percentage of accuracy and less loss to generate the model for a better automatic classification of images using a MURED database containing retinal images already labeled previously with their respective disease. The results show that the model with the ResNet architecture variant InceptionResNetV2 has an accuracy of 49.85%.
 
Keywords—retinal diagnosis, Convolutional Neural Network (CNN), deep learning, machine learning, automated diagnosis


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