  In this manuscript, we automate the procedure of grading of diabetic
retinopathy and macular edema from fundus images using an ensemble of
convolutional neural networks. The availability of limited amount of labeled
data to perform supervised learning was circumvented by using transfer learning
approach. The models in the ensemble were pre-trained on a large dataset
comprising natural images and were later fine-tuned with the limited data for
the task of choice. For an image, the ensemble of classifiers generate multiple
predictions, and a max-voting based approach was utilized to attain the final
grade of the anomaly in the image. For the task of grading DR, on the test data
(n=56), the ensemble achieved an accuracy of 83.9\%, while for the task for
grading macular edema the network achieved an accuracy of 95.45% (n=44).
