## 2 Related Work  
With the rapid development of deep learning technology and high-speed computing hardware, many researchers have successively invested in computer-aided diagnosis (CAD) systems for various diseases. For example, [PERSON] et al. [6; 7] proposed a system for detecting COVID-19, [PERSON] et al. [8] proposed a lung cancer diagnosis system, [PERSON] et al. [9] proposed a breast cancer detection system, etc. CAD systems are crucial in modern clinics to guide the accurate detection and treatment of different diseases. It plays an outstanding role in disease detection, clinical diagnosis, and treatment planning. Glaucoma is an incurable eye disease that causes retinal degeneration. Although glaucoma cannot be cured completely, its progression can be controlled if diagnosed early. Unfortunately, early diagnosis is rare because there are no apparent symptoms in the early stage. Several researchers have attempted to investigate various methods and algorithms for glaucoma detection using fundus images. [PERSON] et al. [10] described several computer-based methods for glaucoma detection. They also introduce various datasets, such as SINDI, SCES, and SIMES, and methodologies for detecting glaucoma, such as detection of glaucoma through optic nerve head, thresholding-based techniques, and level-set-based techniques. [PERSON] et al. [11] analyzed different researchers' optic disc and optic cup segmentation methods and their classification in glaucoma diagnosis. [PERSON] et al. [12] proposed an automatic glaucoma diagnosis method. They use empirical wavelet transform (EWT) on digital images for image decomposition and extract the desired features with correlation entropy, then use the least squares SVM (LS_SVM) to construct the classifier. The glaucoma detection system proposed by [PERSON] et al. [13] converts color images to grayscale images with adaptive histogram equalization, and then convolutes these images with Leung-Malik (LM), Schmid (S) and maximum response (MR4 and MR8) filters. These convoluted textures are used to extract Local Configuration Pattern (LCP) features, followed by k-Nearest Neighbors (kNN) for classification. A method for optic disc (OD) segmentation based on multi-level thresholding was proposed by [PERSON] et al. [14] Retinal vessels are enhanced by convolving the image with a linear filter, and ROIs are achieved by applying a local entropy threshold. To segment OD regions, they implemented multilevel thresholding followed by morphological operations. However, in this technique, the segmentation of OC, which is also essential for glaucoma disease screening, is not considered. Convolutional neural networks (CNNs) are able to obtain information in hierarchical structures from images and thus can be applied to distinguish glaucomatous images from non-glaucoma images. [PERSON] et al. [15] proposed an enhanced U-Net model called G-Net to implement their glaucoma detection system, which is based on the deep learning structure developed by CNNs. A generative adversarial network (GAN) [16] consists of a generator and a discriminator whose purpose is to generate new data similar to some input data. The generator tries to generate data that can fool the discriminator, and the discriminator tries to correctly identify the generated data as fake. [PERSON] et al. [17] trained a conditional generative adversarial network (cGAN) to segment discs from retinal images to detect glaucoma. They used taxonomic diversity and distinction indices to distinguish and classify abnormalities in retinal images. [PERSON] et al. [18] reviewed the literature on the use of GAN technology to diagnose eye diseases in recent years. They described the advantages and disadvantages of this technology as well as future trends.