## 1 Introduction
Recently, deep learning techniques have the ability to discover intricate structures directly from high dimensional data and have been applied in many automated detection systems in medical images. deep learning using convolutional neural network (CNN) was applied to glaucoma identification [3; 5; 6; 20; 21; 22; 23; 24; 25], with or without OD extraction. The studies in [3; 5; 20; 21; 25] classified glaucomatous images in two steps: the OD regions were first extracted and used them as input to deep CNN models. For instance, [PERSON] et al. [3] segmented the OD region first using a _U_-shaped CNN, then the cropped region was transformed into a polar coordinate system and resized to 224 \(\times\) 224 pixels, the input size of pre-trained networks from the ResNet model. Finally, an ensemble method was applied using a majority vote. They achieved accuracy of 83.2% for the SCES dataset and 66.6% for the SINDI dataset. [PERSON] et al. [5] described the methods used by 12 qualified teams that took part in the online REFUGE challenge, which focused on glaucoma classification and OD and OC segmentation, using 1200 images from the REFUGE database. The best performance team, CUHKMED, presented a two-stage approach: firstly, OD region was coarsely segmented, using ResNet-50, then the corresponding region was cropped and input to an ensemble learner of ResNet-50, ResNet-101, ResNet-152 and ResNet-38 models via a majority vote. Sensitivity of 97.5% and AUC of 98.8% were reported. [PERSON] et al. [20] first segmented OD and OC regions using _U_-net, then extracted eight morphologic features and fed them as an input to a random forest classifier. They tested using ORIGA dataset and obtained accuracy of 76.9% and AUC of 83.1%. [PERSON] et al. [21] identified OD region in three steps; region proposal network was applied first to randomly generate a number of rectangular objects then fed the filtered images to a CNN to find the object with the highest score and finally, the bounding box regression was used to locate and extract the OD region. In the second stage, a CNN model was used to classify the OD image. On the ORIGA dataset, they achieved sensitivity of 71.2% and AUC of 87.4%. [PERSON] et al. [25] first applied a _U_-net model to segment the OD and OC and then computed CDR using DRISHTI-GS dataset; they reported only OD and OC classifications with accuracies of 95.8% and 93.0%, respectively. In the recent studies, deep learning methods without need of OD segmentation have proposed. The entire retinal image was fed to deep CNN models [6; 22; 23; 24]. For instance, [PERSON] et al. [6] used five different CNN models--InceptionV3, XceptionNet, VGG16, VGG19 and ResNet50--to classify between normal and glaucomatous images. The XceptionNet model provided the best performance, yielding the accuracy 71.2% for RIM-ONE, 75.25% for DRISTI-GS1 and 70.21% for ACRIMA dataset and AUCs of 85.8%, 80.4% and 76.8% for the same datasets. Similarly, [PERSON] et al. [22] reported five CNN models based on standard CNNs--VGG19, ResNet50, DENet and GoogleNet--from which VGG19 performed best with sensitivity of 87.0%, specificity of 89.0% and AUC of 94.0% for RIM-ONE dataset. [PERSON] et al. [23] first manually cropped the OD region and used blood vessels inpainting and contrast enhancement. Then, the deep activated features were extracted using OverFeat and VGG-S pretrained models and input to logistics regression. They achieved AUC of 76.26% for the DRISTI-GS1 dataset. [PERSON] et al. [24] pre-trained a network based on ResNet model using the whole retinal image. They achieved an AUC of 94.8% using 1364 glaucoma images and 1768 healthy images from a local dataset. In the previous studies, conventional image processing techniques were commonly used for OD segmentation, for example, superpixels methods [14; 16; 17] and Hough transform [15]. Few recent studies segmented OD using deep semantic segmentation techniques, especially _U_-net model.