  Widespread outreach programs using remote retinal imaging have proven to
decrease the risk from diabetic retinopathy, the leading cause of blindness in
the US. However, this process still requires manual verification of image
quality and grading of images for level of disease by a trained human grader
and will continue to be limited by the lack of such scarce resources.
Computer-aided diagnosis of retinal images have recently gained increasing
attention in the machine learning community. In this paper, we introduce a set
of neural networks for diabetic retinopathy classification of fundus retinal
images. We evaluate the efficiency of the proposed classifiers in combination
with preprocessing and augmentation steps on a sample dataset. Our experimental
results show that neural networks in combination with preprocessing on the
images can boost the classification accuracy on this dataset. Moreover the
proposed models are scalable and can be used in large scale datasets for
diabetic retinopathy detection. The models introduced in this paper can be used
to facilitate the diagnosis and speed up the detection process.
