## 1 Introduction  
Glaucoma is a chronic eye disease estimated to be the second leading cause of blindness globally. It is indicated by the degeneration of optic fiber, which leads to gradual optic nerve damage [1, 2]. The number of glaucoma patients reached 80 million in 2020, in which, approximately 50% of people affected by glaucoma are unaware of this eye disease [3]. Irreversible vision loss will occur if the patients are not treated properly. Hence, early screening and gradual proper treatment are necessary to avoid vision loss and to retard the progression of glaucoma disease [4]. Traditionally, the glaucoma early screening procedure is conducted by calculating and analyzing the value of the cup-to-disc ratio (CDR) and disc damage likelihood scale (DDLS) [3, 5]. CDR and DDLS values are the gold standards in diagnosing glaucoma, which define the ratio between the optic cup and optic disc and consist of some range that indicates the level of the damage [3]. To calculate CDR and DDLS, the doctor needs to measure some parameters, such as the diameter of the optic disc (OD) and optic cup (OC), the angle of the optic cup, and the closest distance between the optic cup and optic disc. OD and OC become the major objects used to calculate CDR and DDLS since the degeneration of optic fiber gives a direct effect on the neural rhyme, which enlarges the optic cup area. According to this situation, the optic cup becomes larger than the optic disc, which changes the value of CDR and DDLS [3] (see Figure 1a,b). In addition to this screening procedure, the doctor often uses alternative procedures by analyzing the damage to the retinal nerve fiber layer (RNFL) [5, 6]. The progressive damages to the RNFL are indicated by the loss of nerve fiber around the optic nerve head (ONH). This progressive damage can be relatively well-defined as a texture change in the retinal images (see Figure 1c). An example image in Figure 1c represents the RNFL loss around the optic disc and blood vessel, with an average result of the green and blue channels. Accentually, red green blue (RGB) color space cannot illustrate the RNFL clearly, hence, a different color space or channel should be utilized in an image [6]. In the glaucoma early screening procedure, the calculation and analysis of CDR, DDLS, and RNFL are conducted manually, which needs an expert, more time, and thoroughness of result. As an alternative, several researchers have developed a computer-aided diagnosis (CAD) to assist doctors in diagnosing glaucoma, and to be an alternative solution for the screening process, which needs a large amount of data to be examined. Recently, the machine learning approach became one of the solutions for developing CAD for glaucoma. As proposed by [PERSON] et al. [7], 52 patients consisting of 20 with glaucoma and 32 healthy were classified into two classes by using four classifiers: linear logistic regression, support vector machine (SVM), random forest classifier, and gradient boosting classifier. Their proposed method successfully achieved an accuracy of 98.3%. [PERSON] and [PERSON] [8] proposed a classification model for glaucoma by using a support vector machine. Other automated detection methods for glaucoma also have been researched using several machine-learning methods [9, 10, 11, 12]. Regardless of enthusiasm for machine learning performance, it seems unadaptable, particularly for this big dataset. As a solution, a deep learning approach has been im  
Figure 1: An example of (**a**) OD (marked by a white circle) and OC (marked by a black circle) indicates a healthy optic nerve, (**b**) OD (marked by a white circle) and OC (marked by a black circle) indicates a glaucomatous optic nerve and (**c**) glaucoma location [3, 6]. plemented in the development of CAD for glaucoma. Deep learning (DL) is a powerful learning model that has been widely used in some field studies, especially those which have complex behavior that can be solved with traditional machine learning.