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Vision impairment due to pathological damage of the retina can largely beprevented through periodic screening using fundus color imaging. However thechallenge with large scale screening is the inability to exhaustively detectfine blood vessels crucial to disease diagnosis. In this work we present acomputational imaging framework using deep and ensemble learning for reliabledetection of blood vessels in fundus color images. An ensemble of deepconvolutional neural networks is trained to segment vessel and non-vessel areasof a color fundus image. During inference, the responses of the individualConvNets of the ensemble are averaged to form the final segmentation. Inexperimental evaluation with the DRIVE database, we achieve the objective ofvessel detection with maximum average accuracy of 94.7\% and area under ROCcurve of 0.9283.

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