## 2 Literature Review  
Recently, many deep learning-based algorithms for medical image analysis have been presented to detect glaucoma and deployed due to advances in computer technology. [PERSON] et al. [1] proposed a glaucoma detection model using the ORIGA dataset. They applied the U-Net architecture for segmenting the OD and OC regions from the fundus images, and then used a pretrained DenseNet-201 architecture to extract features from the segmented regions. They also used deep convolutional neural networks (DCNNs) to classify the images as glaucomatous or normal, achieving an accuracy rate of 98.82% in training and 96.90% in testing. [PERSON] et al. [2] developed Mask-RCNN for clustering ODand OC lesions from fundus images using DenseNet-77 as a backbone in the Mask-RCNN architecture. The glaucoma segmentation approach was effective, but improvements were needed, with average precision, F-measure, recall, and IOU values of 0.96, 0.97, 0.96, and 0.97, respectively. [PERSON] et al. [3] proposed the ODGNet method for OD localization and glaucoma classification. They used a saliency map for OD localization and a shallow CNN to separate OD and non-OD regions. They fed segmented OD regions into transfer learning models like AlexNet, ResNet, and VGGNet for glaucoma diagnosis. The combination improved ODGNet's performance, achieving 95.75% accuracy. [PERSON] et al. [9] developed a network for glaucoma detection that used the OD and OC lesions as indicators. The EfficientNet-B0 model was used to extract features from fundus images, with a unique bidirectional feature module for multiple feature integration, and the EfficientDet-D0 model for simultaneous glaucoma localization and class prediction. Their approach achieved an average accuracy of 97.2% on the ORIGA dataset and 97.96% and 98.21% on the RIM-ONE and high-resolution fundus (HRF) datasets, demonstrating its robustness in classifying glaucoma. [PERSON] et al. [10] used the local binary pattern (LBP) approach to extract features from fundus images, retrained the AlexNet model for glaucoma diagnosis, and increased the training data size using an LBP-based data augmentation strategy to avoid overfitting. The model achieved 98.90% accuracy on the RIM-ONE fundus image database. [PERSON], [PERSON], et al. [11] proposed a deep learning framework for detecting glaucoma from fundus images. The framework involves three steps: locating the OD, segmenting the OD and OC, and classifying the glaucoma risk. Using models like U-Net, SegFormer, and MobileNet V2, the framework achieves high dice scores of 0.98 and 0.91 for segmentation and 95.47% accuracy for glaucoma detection. The framework also shows high sensitivity and area under the curve (AUC) scores for glaucoma detection using ResNet-18 and VGG-16 on different datasets using ResNet-18 and VGG-16 on different datasets. [PERSON] et al. [12] developed GlauNet, a CNN that consisted of three convolutional layers to extract features and five fully connected layers for classification. The collected dataset comprised OCTA, OD, and OCT testing. The model was trained using a dataset of 258 eyes with glaucoma and 439 eyes without glaucoma, achieving a sensitivity of 88.9% and a specificity of 89.6% with an AUC of 0.89. [PERSON] et al. [13] presented a glaucoma classification framework called MTRA-CNN and conducted four experiments to assess its effectiveness. They determined that ResNet50 was the most effective CNN for diagnosing glaucoma. Additionally, they found that the residual attention (RA) block improved performance, and the multi-scale transfer learning strategy outperformed other approaches. The framework achieved an accuracy of 86.8% in classifying normal eyes and different stages of glaucoma, demonstrating its superiority. [PERSON] et al. [14] introduced a method that uses an ensemble CNN to precisely identify glaucoma by segmenting the optic disc (OD) and optic cup (OC). The approach utilizes a modified attention U-Net structure with pretrained ResNet34, Inceptionv3, and DenseNet121 backbones.