## 1 Introduction
In the study, the region of interest (ROI) is extracted by determining the optic disk (OD) center in fundus images and vessels, classified using intensity differences in different color spaces. The average intensity, contrast and roughness features of the vessels in RGB, LAB and HSV color spaces were extracted, and classification was performed with SVM. In the tests, the mean density, contrast and roughness values for arteries were 124.29 \(\pm\) 26.09, 6.40 \(\pm\) 1.43 and 0.0006 \(\pm\) 0.0003, respectively, while for veins these values were 110.61 \(\pm\) 27.79, 9.42 \(\pm\) 2.54 and 0.0013 \(\pm\) 0.0007, respectively. The proposed method was tested on 25 fundus images and an accuracy of 81.3% is achieved. [PERSON] et al. [16] developed a system called HYPER-RETINO to classify five stages (normal, mild, moderate, severe, malignant) of HR. The model uses semantic and object-based segmentation techniques to detect HR lesions and classifies them using DenseNet architecture. They tested the model on 1400 fundus images and achieved 92.6% accuracy, 90.5% sensitivity and 91.5% specificity. [PERSON] et al. [17] developed a hybrid DL architecture to classify HR severity levels. In the study, HR was classified into four classes (normal, mild, moderate, severe) by creating an expert-labeled HRSG dataset. The model aims to capture both local and global contextual information by combining transfer learning with the pre-trained ResNet-50 and the improved Vision Transformer (ViT) architecture. With the Decoupled Representation and Classifier (DRC) method, class imbalance is removed and the overall diagnostic accuracy of the model is improved. In the tests, the proposed method outperformed existing HR classification methods, achieving 96.88% accuracy, 94.35% sensitivity, 97.66% specificity and 94.42% F1 score. Related studies in the literature are summarized in Table 1. According to Table 1, it is seen that many AI-based studies have been carried out in the diagnosis of eye diseases, especially focusing on the detection of diseases such as DR, glaucoma and macular degeneration. However, studies on HR are limited and the literature does not provide a sufficient approach in this field. In addition, existing studies have some important limitations. Most of the previous studies, for example, use feature extraction based on a single CNN model. However, a single model may not be sufficient to capture diverse and discriminative features at different stages of HR. Moreover, although some studies integrate ML based classifiers, they do not examine the effectiveness of feature selection methods, which may lead to redundant information content in the extracted features. Furthermore, although some studies report high accuracy rates, they do not provide a comprehensive analysis of how the model generalizes across different HR severity levels. Whether the model is overfitting, its performance in different patient groups or accuracy rates at specific stages are not examined in detail. These shortcomings indicate the need to develop a more reliable and generalized AI-based system for the diagnosis of hypertensive retinopathy. The main contributions of this study are as follows:  
* A comprehensive literature analysis was conducted to examine existing AI-based studies in the diagnosis of eye diseases. * Fourteen different CNN models commonly used in the literature were trained on a custom HR dataset and the three best-performing models were determined. * Deep features extracted from the top three CNN models were combined to create a more robust feature set. * The combined features were classified by ML algorithms (SVM, RF and XGBoost). * GA, ABC, PSO and HHO methods were used in the feature selection process. * The classification performance of the model was analyzed using extensive experiments and different evaluation metrics. The remaining sections of this paper are organized as follows:  
Section 2 describes the materials and methods used in this study, including dataset details, DL and ML techniques.