Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images

TL;DR

This paper introduces an ensemble of deep convolutional neural networks to improve the accuracy of retinal vessel detection in fundus images, aiding early diagnosis of eye diseases.

Contribution

It presents a novel ensemble learning framework that combines multiple CNNs for more reliable vessel segmentation in fundus images.

Findings

Achieved 94.7% average accuracy on DRIVE database

Attained an area under ROC curve of 0.9283

Demonstrated improved detection of fine blood vessels

Abstract

Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images. An ensemble of deep convolutional neural networks is trained to segment vessel and non-vessel areas of a color fundus image. During inference, the responses of the individual ConvNets of the ensemble are averaged to form the final segmentation. In experimental evaluation with the DRIVE database, we achieve the objective of vessel detection with maximum average accuracy of 94.7\% and area under ROC curve of 0.9283.