Retinal vessel segmentation based on neural network application

Automated segmentation of retinal vessels plays an important role in diagnosing diabetic diseases. In this paper, I propose the convolutional neural network (U-Net) to yield more precise segmentations of retinal vessels from various fundus images. The performance of this neural network was first tested on the DRIVE database, and it achieved a relatively high score in terms of area under the Receiver Operating Characteristic (ROC) curve, with an astounding result of 0.9790, in comparison to the other existing methods published. On the STARE database, this method also yield satisfying results. This shows that the U-net architecture is a very effective and efficient model to aid in the early diagnosis of diseases.Bachelor of Engineering (Electrical and Electronic Engineering