Retinal blood vessel segmentation using transfer learning on unet

Manuel segmentation of the retinal blood vessel is time-consuming and there are different conclusions among the ophthalmologists hence creating an automated approach can improve the analysis of the images in order to detect diseases like hypertension. Deep Learning models have shown great performance in the last decade. In this thesis, we constructed the network with UNET, a Convolutional Neural Network-based architecture particularly efficient for medical images. Since the DRIVE dataset, which is freely available, comprises insufficient numbers of training and test samples, we created our method using Transfer Learning, a method that uses the weights from the previously trained network rather than starting the learning with random values. We used several state-of-the-art pre-trained network designs such as VGG, ResNet, and EfficientNet in two different sizes. With the VGG19 architecture, we were able to achieve 96.84% accuracy by freezing the top 12 layers of the network. This promising result shows that this approach can be used in real-world scenarios, with improved accuracy.