## 3 Experimental Results  
This study was experimented using Matlab 2019b in Window 10 and Nivida T166 Ti with core i7. We tested our work on 2787 retinal images from the five public databases: REFUGE, ACRIMA, ORIGA, RIM-ONE and DRISTI-GS1. REFUGE dataset contains 1200 retinal images captured using either Seiss Viscucam or Canon CR-2 from Chinese patients. The images were centered on both macular and OD visible, for the purpose of examining optical head nerve damage and retinal nerve fiber layer defects. The dataset contains pixel-wise annotations of OD and OC and classification labels of normal and glaucomatous marked by seven ophthalmologists. Poor quality images were discarded by the ophthalmologists during image labeling. The dataset was predefined into three subsets: training (400 images), validation (400 images), and testing (400 images), and each set contains an equal proportion of glaucomatous (10%) and non-glaucomatous (90%). ACRIMA dataset was created by the Ministerio de Economia y Competitividad of Spain for the classification of glaucomatous images. The dataset contained 309 normal and 396 glaucomatous images captured by Topcon TRC retinal camera with a 35\({}^{\circ}\) field of view (FOV). The images were taken from the eyes dilated and centered in OD. Only high-quality images were selected to avoid artefacts, noises and poor contrast. Two glaucoma experts manually labeled the images into either normal or glaucoma. ORIGA dataset is a part of SiMES dataset which was collected from 3280 Malay adults aged 40 to 80 and annotated by the trained professionals from Singapore Eye Research Institute. A total of 650 images were selected with the manual segmentation of OD and OC regions. It also provides CDR and labels for each image as glaucomatous or healthy. The RIM-ONE dataset was created for optic nerve head evaluation and glaucoma disease classification. It composed of 169 images which were captured and cited from three different hospitals in Spain (Hospital Universitario de Canarias, Hospital Clinico San Carlos and Hospital Universitario Miguel Servet) to guarantee the problems in image acquisition. All images are non-mydriatic retinal photographs captured with specific flash intensities. Five experts collaborated to annotate and label the retinal images into 3 classes: healthy, suspicious, and glaucoma. As suspicious case is out the scope of this study, we picked only 131 images (92 healthy and 39 glaucomatous images). DRISHTI-GS1 dataset was collected at Aravind eye hospital, Madurai and consists a total of 101 images (31 normal and 70 glaucomatous images). It is divided into 50 training and 51 testing images. All images were taken with the eyes dilated and centered in OD with 35\({}^{\circ}\) FOV. Poor quality images were discarded in terms of contrast, noise and position of OD. Four glaucoma experts with different clinical experiences examined and labeled each image into either normal or glaucoma. Table 3 summarizes the main characteristics of each dataset. All these datasets are collected for the purpose of optic nerve head assessment and glaucoma detection. The images in all datasets focus on OD region, whereas the images in REFUGE dataset were centered on both macula and OD visible. During the labeling process, only high-quality images were selected due to artefacts, noises, poor contrast and OD's position, etc. As the images from different datasets were taken from different ethnicity by different devices and different FOV, we separately tested the proposed methods using each dataset. Our method consists of two stages: OD segmentation and glaucoma classification. First, OD is detected and segregated from the full retinal images using DeepLabv3+ with five different backbone networks. Using the segmented OD, three deep learning-based methods were developed to identify glaucomatous images. For statistical evaluation of OD segmentation stage, only REFUGE dataset is used as it provided pixel-wise annotation of OD region.