Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
This work addresses the challenge of large-scale screening for retinal diseases, which is incremental as it applies an ensemble approach to an existing deep learning method.
The paper tackled the problem of detecting fine blood vessels in fundus images for disease diagnosis by using an ensemble of deep convolutional neural networks, achieving a maximum average accuracy of 94.7% and an area under ROC curve of 0.9283 on the DRIVE database.
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.