## 2 Related Work
### Classification of Glaucoma  
[PERSON] et al. [14] proposed an automatic glaucoma detection system that is based on deep learning (DL) and consists of two stages. In the first stage, DeepLabv3+ and Mobile Net are used as feature encoders and segmentation OD. In the second stage, three different Deep CNNs were used to classify the segmented OD region as normal or glaucoma. [PERSON] et al. [15] studied and compared several deep learning models VGG16, VGG19, ResNet, DenseNet, InceptionV3, Inception ResNet, Xception, and NASNetMobile, and applied preprocessing techniques such as cropping, and histogram equalization to the data in their methods. [PERSON] et al. [16] used the mask color of funds images as a segmentation model to crop the optic disk area. Then, they created an attentive approach to detect glaucoma. [PERSON] et al. [17] presented a method for glaucoma detection using a CNN architecture with 18 layers. Image resizing and data augmentation techniques were applied in the preprocessing phase. [PERSON], et al. [18] used different pre-trained CNN models in their study, including VGG19, GoogleNet, ResNet50, and DenseNet. They applied preprocessing techniques, including optic disc localization and ROI detection. [PERSON] et al. [19] presented a novel approach for glaucoma detection that combines deep learning and active learning methods. The approach uses an extracted ROI region. In addition, the model generated interpretable heat maps to assist in decision-making for glaucoma classification. [PERSON] et al. [20] presented a method for glaucoma detection that includes CLAHE-based preprocessing, followed by ROI segmentation using the green channel data, and identification of OD and OC via a modified kernel fuzzy c means algorithm. [PERSON] et al. [21] presented an attention-based CNN model for glaucoma classification, which includes three stages: identification of the ROI, localization of the pathological area using the attention map, and classification as normal or glaucomatous. A two-stage method for optic disc localization and glaucoma classification from fundus images was developed by [PERSON] et al. [22]. In the first stage, the localization of the optic disc is achieved using a Recurrent Convolutional Neural Network (RCNN). In the second stage, a CNN is used to classify normal and glaucoma. [PERSON] et al. [23] presented glaucoma detection using a CNN architecture. Their methodology involves ROI selection. [PERSON] et al. [24] used AlexNet CNNs to extract features from raw fundus images. These feature learners on the images were then classified into normal and glaucomatous types using a support vector machine (SVM) classifier. [PERSON] [25] developed and implemented a combination of an unsupervised CNN architecture and a deep belief network model. During the preprocessing phase, ROI was extracted from the green channel image. The CNN was then trained using these ROI images. A new DL architecture, the so-called \"Vision Transformers\", has recently attracted much attention [26]. There are relatively few studies that focus specifically on distinguishing glaucoma from normal eyes using color fundus photographs. [PERSON] et al. [27] investigated the effectiveness of various vit-based single models (such as Swin Transformer, CrossFit, and cairt) and vit-based ensemble models using raw color fundus images for glaucoma detection. [PERSON] et al. [28] used GLIM-Net, which is a Glaucoma Forecast Transformer based on irregularly sampled to predict the future onset of glaucoma for patient images.