## 1 Introduction  
The eye is one of the most complex and vital organs in the human body. It enables people to perceive their surroundings and process visual information to make sense of it. As the main organ of the sense of sight, the eye functions through the harmonious functioning of sensitive structures such as the cornea, lens, retina and optic nerve [1]. The healthy functioning of these structures directly affects the quality of life of the individual. However, many factors such as genetic factors, aging, infections, chronic diseases such as diabetes, trauma and unhealthy living habits can negatively affect eye health and lead to various diseases [2]. Among eye diseases, serious conditions such as glaucoma, macular -degeneration, diabetic retinopathy (DR) and HR are among the leading causes of vision loss worldwide. These diseases can lead to irreversible vision loss if not diagnosed and treated early. Therefore, early diagnosis of eye diseases is critical to protect the quality of life of individuals and to successfully plan treatment processes [3]. Medical imaging techniques have become an integral part of diagnosis and treatment processes in modern medicine. In particular, methods such as Optical Coherence Tomography (OCT) allow eye diseases to be detected at early stages by imaging sensitive structures of the eye such as the retina, macula and optic nerve with high resolution [4]. However, manual evaluation of these images is time-consuming, subjective and is prone to error. In addition, the lack of specialists, especially in rural areas, makes this process even more difficult. For all these reasons, there is a growing need for technologies that automate the diagnosis of eye diseases and make the process both faster and more accessible. Rapid advances in hardware and software technologies have led to the development of numerous Computer Assisted Diagnosis (CAD) systems in this field [5]. In particular, Artificial Intelligence (AI) methods are emerging as effective tools for the diagnosis of eye diseases. There are a number of studies in the literature on AI-based automatic diagnosis of eye diseases. [PERSON] et al. [6] performed glaucoma detection on gray channel fundus images using ResNet-50 architecture. Fundus images were processed with grayscale technique and optimized for model training by focusing on the optic disk. The performance of the ResNet-50 model was improved by implementing data augmentation techniques and transfer learning methods. The model was evaluated on the G1020 dataset and achieved high performance results such as 98.48% accuracy, 99.30% sensitivity, 96.52% specificity, 97% AUC and 98% F1-score. [PERSON] et al. [7] proposed a model combining Flexible Analytic Wavelet Transform (FAWT) and Gaussian-Kuzmin distribution-based Gabor filters (GKDG) to develop an automatic diagnostic tool for the diagnosis of glaucoma. They decompose the green channel components of fundus images using FAWT and extract features from these sub-bands using GKDG filters. Neighborhood Component Analysis (NCA) is applied for dimensionality reduction and the features are classified with LS-SVM algorithm. The proposed model achieved 95.84% accuracy, 97.17% specificity and 94.55% sensitivity in the experiments on the RIM-ONE dataset. [PERSON] et al. [8] developed a framework for the diagnosis of glaucoma using fundus images. A customized CNN model designed to extract features from the images. In the dimensionality reduction stage, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques were used together. For classification, the Extreme Learning Machine (ELM) method was employed. To enhance the performance of ELM, a Modified Particle Swarm Optimization (MOD-PSO) algorithm was applied to optimize the input weights and biases. The model's performance was evaluated using five-layer cross-validation on the G1020 and ORIGA datasets. The authors obtained success on the G1020 dataset with 97.80% accuracy, 94.92% sensitivity and 98.44% specificity.