Convolutional Neural Network based Retinal Vessel Segmentation

Abstract

In human eye, the state of the blood vessel is a crucial diagnostic factor. The segmentation of blood vessel from the fundus image is difficult due to the spatial complexity, adjacency, overlapping and variability of blood vessel. The detection of ophthalmic pathologies like hypertensive disorders, diabetic retinopathy and cardiovascular diseases are remain challenging task due to the wide-ranging distribution of blood vessels. In this paper, Stacked Autoencoder and CNN (Convolutional Neural Network) technique is proposed to extract the blood vessel from the fundus image. Based on the experiments conducted using the Stacked Autoencoder and Convolutional Neural Network gives 90% & 95% accuracy for segmentation.