## 2 Related Work  
Automated vessel segmentation is generally an understandable and well-known problem [10; 11]. Basically, concerning the eye, the primary purpose is to separate the pixels of a fundus image into two categories: vessel pixels and none vessel pixels. Several research attempts have been made in the literature for accurate, automatic fundus image segmentation and evaluation. A detailed overview of methods, systems and approaches can be found in the works presented in [12; 13]. The deep learning category mainly belongs to methods that solve the problem with classification algorithms. The pixel classification with specific characteristics is a well-known machine learning technique that classifies the pixels of an image into one or more classes. The classification of the pixels is usually performed using a Supervised Learning technique. Vessel segmentation with the help of supervised learning requires two main steps for making the algorithm work properly. In the first step, the algorithm learns statically to classify the pixels correctly from already known classifications. In the second step, which tests how well our algorithm performs, the algorithm classifies images that have never been examined. The first step concerns the training phase, and the second one concerns the testing. Then, for the correct evaluation of the classification algorithm's supervised functionality, the data used for training and the one for the evaluation must be completely different. In the work presented in [14], the authors present an approach where they face the vessel detection task as a classification problem and develop a CNN (Convolutional Neural Network). Their network consists of two convolution layers, two pooling layers, one dropout layer and a loss layer and is formulated to automatically extract the featureswithout any preprocessing steps. The proposed CNN achieves 91.99% accuracy and 96.52 AUC on the DRIVE dataset and 92.20% accuracy and 94.40 AUC value on the STARE data set, respectively. Authors in the work presented in [15] present a fully convolutional neural network model used for the blood vessel segmentation task. Moreover, the authors performed five prepossessing steps on the RGB fundus images: extraction of the green channel, normalisation, gamma adjustment, and contrast-limited adaptive histogram equalisation. Finally, the reduction pixels value to the 0-1 range. Then the input given to the 1 st convolutional layer is mainly a 1 \(\times\) 28 \(\times\) 28 patch extracted from the preprocessed fundus photo. Their model consists of 8 layers. The first two are convolutional layers with 32 filters, the third is a max-pooling layer, and after that, the fourth and the fifth are convolutional layers with 64 filters. The sixth one is an upsampling layer, and the seventh and the eighth are convolutional layers with the same size padding and 32 filters. Finally, the output dimensions are 1 \(\times\) 28 \(\times\) 28. The model reports high performance and significantly on the DRIVE dataset reported 95.33% accuracy and 97.4% AUC score. [PERSON] et al. introduced two efficient methods for vessel segmentation in retinal images [16]. Their study approached the segmentation problem using a Fuzzy classifier and a U-net autoencoder with Residual blocks. The Fuzzy classifier method extracted features by considering a fundus image's mean and median properties, using a fuzzy interface to extract the vessels and post-processing with multi-level threshold and morphological operation. The second technique utilised an autoencoder model to construct masked versions of the retinal images, highlighting only the blood vessels. Both methods achieved state-of-the-art performance, with the Fuzzy system algorithm achieving 95.72% accuracy on the DRIVE test data and the autoencoder network achieving 96.75% accuracy.