## 2 Related Work
Their work performed various preprocessing steps on the retinal fundus images, including green channel extraction, complement operation, CLAHE to improve vessel contrast, Gaussian filter to reduce noise, and normalisation by subtracting the background image from the CLAHE applied to the image. Another work was the construction of an ensemble of deep convolutional neural networks by [PERSON] et al. [17]. More precisely authors developed a computational imaging framework for detecting blood vessels in fundus-coloured images using deep and ensemble learning. They used an ensemble of 12 deep convolutional neural networks to segment vessel and non-vessel areas of the image. Their work explained that ensemble learning involves using multiple models to solve an artificial intelligence problem. Their model consisted of three convolutional layers and two fully connected layers, and they trained it using randomly selected patches from the training images. They evaluated their model on the DRIVE dataset and achieved a maximum average accuracy of 94.7% and an area under the curve of 92.83% for vessel detection. Moreover, [PERSON] et. al. [18] introduced the Deformable U-Net (DUNet), which uses U-shape architecture to exploit local features of retinal vessels for end-to-end segmentation. They applied three preprocessing steps to the original images: normalisation, CLAHE operation, and gamma correction, and used 48x48 patches to reduce overfitting during training. The DUNet consists of an encoder, a decoder, and a framework, with deformable convolutional blocks, used to model vessels of various shapes and scales. The blocks consist of a convolution offset layer, a convolution layer, a batch normalisation layer, and an activation layer. The model was evaluated on four public datasets (DRIVE, STARE, CHASE_DB1, HRF), achieving a global accuracy of 95.66, 96.41, 96.10, and 96.51 and an AUC of 98.02, 98.32, 98.04, and 98.31 for vessel segmentation. A noticeable related work in the field concerns the RV-GAN model introduced by [PERSON] et al. [19]. More specifically, the RV-GAN architecture has a new multi-scale generative architecture, which uses two generators and two multi-scale autoencoding discriminators for better micro-vessel localisation and segmentation. They used two generators since it produces high-quality domain-specific retinal image synthesis. The proposed generators and discriminators consist of both downsampling and upsampling blocks. The downsampling block comprises a convolution layer, a batch-norm layer and a Leaky-ReLU activation function consecutively. In contrast, the upsampling block consists of a transposed convolution layer, batch-norm, and Leaky-ReLU activation layer successively. To avoid the loss of fidelity, [PERSON] et al. introduced novel weighted loss, which incorporates and prioritises features from the 'discriminator's decoder over the encoder. By this, combined with the fact that the 'discriminator's decoder attempts to determine actual or fake images at the pixel level, it better preserves macro and microvascular structure. The evaluation metrics of RV-GAN are very promising for DRIVE, STARE and CHASE_DB1 datasets. The model achieves AUC of 98.87, 99.14, and 98.87 and global accuracy of 97.90, 96.97 and 97.54, respectively. Another GAN architecture proposal was introduced in the work presented in [20], where authors introduced the M-GAN model. This new conditional generative adversarial network uses ACE preprocessing and a generator and discriminator to conduct retinal vessel segmentation. A preprocessing based on ACE is applied to the input fundus image. ACE mimics appropriate adaptive behaviours of the human visual system, such as colour constancy and lightness constancy [21]. The M-generator has deep residual blocks for robust segmentation, and the M-discriminator has a deeper network for efficient adversarial model training. A multi-kernel pooling block is added to support scale invariance, and the M-generator and M-discriminator both have downsampling layers to extract features.