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In this manuscript, we automate the procedure of grading of diabeticretinopathy and macular edema from fundus images using an ensemble ofconvolutional neural networks. The availability of limited amount of labeleddata to perform supervised learning was circumvented by using transfer learningapproach. The models in the ensemble were pre-trained on a large datasetcomprising natural images and were later fine-tuned with the limited data forthe task of choice. For an image, the ensemble of classifiers generate multiplepredictions, and a max-voting based approach was utilized to attain the finalgrade 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 forgrading macular edema the network achieved an accuracy of 95.45% (n=44).

Ensemble of Convolutional Neural Networks for Automatic Grading of Diabetic Retinopathy and Macular Edema