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

TL;DR

This paper presents an ensemble of CNNs using transfer learning to automatically grade diabetic retinopathy and macular edema from fundus images, achieving high accuracy despite limited labeled data.

Contribution

It introduces a transfer learning-based ensemble approach with max-voting for grading eye diseases from fundus images, addressing data scarcity issues.

Findings

Achieved 83.9% accuracy for diabetic retinopathy grading.

Achieved 95.45% accuracy for macular edema grading.

Demonstrated effectiveness of ensemble CNNs with transfer learning.

Abstract

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).