## 1 Introduction  
The World Health Organization estimated that, in 2016, 64 million people were living with glaucoma and that it will increase to 95 million by 2030 [1]. Glaucoma is an eye disease, which damages the optic nerve and can lead to blindness if left untreated. It is currently the main cause of irreversible vision loss and is caused by high intraocular pressure pushing against the optic nerve in the eye [2]. The damaged nerve fiber leads to a larger optic cup region and thinning of the inferior rim around the optic nerve. Progression of the disease can lead to 'pale disc' and disc hemorrhage. Angle-closure glaucoma and open-angle glaucoma are the two common glaucoma types and present different warning signs. Angle-closure glaucoma causes very noticeable symptoms, for example, blurred vision, severe eye pain, sudden sight loss, light halos and more. On the other hand, open-angle glaucoma slowly progresses and shows no symptoms, until peripheral vision is lost thus it is called \"the sneak thief of sight\". Therefore, regular eye examination once per year is essential and recommended for early glaucoma screening, particularly for people, over 40 years old, as the number of patients increases sharply with age and for people with early warning signs. Clinically, intraocular pressure measurement, visual field testing optic nerve head assessment are currently used to screen glaucoma, but only optic nerve head assessment can detect early stage glaucoma [3]. Thus, optic nerve assessment in retinal images becomes an essential and standard test for glaucoma detection. Glaucoma affects the optic disc (OD) region, and causes OD abnormalities, for example enlarging cup to disc ratio, a pale color, hemorrhage or changes in the vicinity of the OD. Figure 1 shows the noticeable differences of the OD in a healthy and a glaucoma eye. Various stages of glaucoma can be observed in Figure 2.  
In routine clinical examinations, glaucoma can be detected by examining the abnormalities of OD in direct or indirect ways [4]. An ophthalmologist can directly examine the eye using an ophthalmoscope. Alternatively, a trained technician uses a fundus camera to capture fundus images and then an ophthalmologist visually examines the digital image. Glaucoma screening is expensive, labor-intensive, time-consuming and prone to human errors. It needs specialists with a high degree of expertise. In developing countries and rural areas, generally, there are insufficient eye specialists  
Figure 1: Images of a healthy retina (left) and one with glaucoma (right). Figure 2: Grading of glaucoma diseases: (**a**) healthy OD; (**b**) Mild Glaucoma; (**c**) Moderate Glaucoma and (**d**) severe glaucoma. available. To deal with a large number of data and reduce human errors in screening, it is important to develop an automatic glaucoma detection system, which can deliver affordable, accurate, fast and interpretable diagnoses. To develop an automated system a large set of retinal images is required. Nine datasets are readily available: REFUGE [5], ACRIMA [6], ORIGA [7], RIM-ONE [8], DRISHTI-GS1 [9], HRF [10], SiMES [11], SCES [12] and SiNDI [13]. The key characteristics of each dataset are listed in Table 1.  
Automated retinal image analysis to detect glaucoma has been researched intensively in recent years with variable success. The methods vary from simpler machine learning approaches to sophisticated and advanced deep learning approaches. Most glaucoma detection algorithms have two common steps--the region of interest segmentation and classification for the presence of glaucoma. The simpler machine learning approach may first segment OD and Optic Cup (OC) regions, and then measure cup to disc ratio (CDR) or extract hand-crafted features to determine whether the image contains glaucoma or not [14; 15; 16; 17; 18; 19]. [PERSON] et al. [14] segmented the OD region, using superpixel based segmentation, and then CDR was computed based on the change of intensity within the cropped OD image. The method was tested on two