  In this project, we developed a deep learning system applied to human retina
images for medical diagnostic decision support. The retina images were provided
by EyePACS. These images were used in the framework of a Kaggle contest, whose
purpose to identify diabetic retinopathy signs through an automatic detection
system. Using as inspiration one of the solutions proposed in the contest, we
implemented a model that successfully detects diabetic retinopathy from retina
images. After a carefully designed preprocessing, the images were used as input
to a deep convolutional neural network (CNN). The CNN performed a feature
extraction process followed by a classification stage, which allowed the system
to differentiate between healthy and ill patients using five categories. Our
model was able to identify diabetic retinopathy in the patients with an
agreement rate of 76.73% with respect to the medical expert's labels for the
test data.
