  In this work, deep learning algorithms are used to classify fundus images in
terms of diabetic retinopathy severity. Six different combinations of two model
architectures, the Dense Convolutional Network-121 and the Residual Neural
Network-50 and three image types, RGB, Green, and High Contrast, were tested to
find the highest performing combination. We achieved an average validation loss
of 0.17 and a max validation accuracy of 85 percent. By testing out multiple
combinations, certain combinations of parameters performed better than others,
though minimal variance was found overall. Green filtration was shown to
perform the poorest, while amplified contrast appeared to have a negligible
effect in comparison to RGB analysis. ResNet50 proved to be less of a robust
model as opposed to DenseNet121.
