## 1 Introduction
On the ORIGA dataset, 98.46% accuracy, 97.01% sensitivity and 98.96% specificity values were obtained. [PERSON] et al. [9] developed a DL framework for the early detection of glaucoma called DeepGD. For the classification of fundus images, EfficientNetB4 and snapshot ensemble methods were used, and Aquila Optimization (AO) algorithm was applied for hyperparameter optimization. The classified images were segmented with V-Net and 99.35% accuracy, 99.04% precision, 99.19% specificity, 98.89% recall and 98.97% f1-score values were obtained. [PERSON] [10] proposed a feature extraction-based approach to classify retinal fundus images into age-related macular degeneration (AMD), DR, cataract, glaucoma and normal. They used fundus images from the Ocular Disease Intelligent Recognition (ODIR) dataset. The images were processed using maximum entropy transform to optimize both quality and information content. The feature extraction was performed using a CNN consisting of two convolutional layers and a maximum pooling layer. The hyperparameters of the CNN are optimized with the Flower Pollination Optimization Algorithm (FPOA). After feature extraction, classification is performed using a multi-class SVM. The proposed method achieved 98.30% accuracy, 95.27% recall, 98.28% specificity and 93.3% F1 score. [PERSON] et al. [11] developed a ViT-based segmentation model to detect non-exudative macular neovascularization (neMNV) in AMD patients using OCT images. In the study, the model was trained using 125,500 OCT images and the results were compared with human performance. The model achieved 82%, 90%, 79% and 91% sensitivity, specificity, PPV and NPV, respectively. Compared to human evaluations, the ViT-based model provided higher accuracy and performed particularly strongly in patients with late AMD. [PERSON] et al. [12] developed a model called Ranking-MFCNet for cataract grading from OCT images. By combining multiscale feature calibration and a ranking-based approach, the model aims to classify six different severity levels of cataracts more accurately. The model uses the eaMFC module, which combines the multiscale attention mechanism with external attention layers to provide a better representation of features to distinguish neighboring severity levels. In the experiments performed on the IOLMaster-700 dataset, the model achieved an accuracy of 88.86%, sensitivity 89.08%, precision 90.15% and F1-Score 89.49%. [PERSON] et al. [13] developed a Swin-TransformerV2-based model for DR diagnosis called STMF-DRNet. The model has a multi-branch structure that allows the extraction of global, local and fine-grained features and aims to improve classification performance by integrating hybrid attention mechanisms and category-based attention mechanisms. In addition, Attention Based Object Localization Module (AOLM) and Attention Based Patch Processing Module (APPM) were implemented to detect lesion regions and reduce noise. The proposed model achieved 77% accuracy, 77% sensitivity, 94.2% specificity, 77% F1-Score and 87.7% kappa value on the clinical dataset. [PERSON] et al. [14] introduced OCT Dataset for Image Based DL Methods (OCTDL), an open access dataset for DL methods based on OCT imaging techniques. The dataset covers a variety of retinal diseases such as (AMD), diabetic macular edema (DME), epiretinal membrane (ERM), retinal artery occlusion (RAO), retinal vein occlusion (RVO) and vitre-omacular interface disease (VID). They also performed experimental studies on OCTDL with ResNet50 and VGG16 models. As a result of these studies, ResNet50 achieved 84.6% accuracy, 89.8% precision, 84.6% sensitivity and 86.6% F1 score, while VGG16 achieved 85.9% accuracy, 88.8% precision, 85.9% sensitivity, 86.9% F1 score and 97.7% AUC. The studies conducted on HR detection in the literature are as follows. [PERSON] et al. [15] developed a method that classifies retinal vessels into arteries and veins to calculate the arterio-venous ratio (AVR) for HR detection.