## 1 Introduction  
Glaucoma, the second most common cause of blindness worldwide, is projected to affect approximately 111.8 million individuals by 2040 [1]. It is an eye disease that damages the optic nerve, often due to an imbalance of intraocular pressure (IOP) within the eye. The damage typically results in the enlargement of the optic disc (OD) and the optic cup (OC), which are specific regions of the retina where the optic nerve exits the eye [2]. If left untreated, glaucoma can lead to irreversible vision loss [3]. Early detection and treatment are crucial to prevent further deterioration and ensure optimal eye care. The diagnosis of glaucoma is based on various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and fundus images. Fundus images show a clear view of the fovea, OD, macula, OC, veins, arteries, and other parts of the retina. They have demonstrated potential in diagnosing eye diseases [4]. Artificial intelligence (AI) learning techniques, including deep learning (DL) and convolutional neural networks (CNNs), are being applied to various medical solutions, including medical image analysis. Current computer-aided diagnosis (CAD) systems based on AI have the ability to examine clinical images and provide a precise diagnosis comparable to that of experienced ophthalmologists. The existing literature on glaucoma detection encounters multiple challenges, including imbalance datasets, model complexity, computational efficiency, and clinical integration challenges. The automation of glaucoma detection would enable early detection and preventing vision loss. This study aims to investigate the optimal performance of state-of-the-art DLmodels for automated glaucoma detection from enhanced retinal images. The techniques conduct segmentation and classification tasks that are tested on four distinct public datasets, ORIGA, HRF, RIM-ONE DL, and REFUGE, to assess the model's capacity to generalize. Medical image segmentation splits an input image into pixel groups to extract information about lesions or organs, known as the region of interest [5; 6]. This study explores medical image segmentation using the widely used CNN architecture, U-Net [1], to segment both OD and OC. Furthermore, medical applications extensively apply transfer learning, a technique that reuses pretrained models trained on large-scale datasets [7]. This approach is particularly beneficial due to the limited availability of datasets and computational resources, allowing developers to save time and modify existing models to suit specific needs [8]. Despite advancements in deep learning algorithms for glaucoma detection, considerable challenges remain, including achieving accurate segmentation, ensuring model generalizability across varied datasets, and adjusting data augmentation techniques. Developing automated glaucoma detection systems is needed, with a focus on improving segmentation accuracy and adapting models to diverse clinical settings. Our research evaluated the U-Net model utilizing five pretrained backbones, including ResNet34, ResNet50, VGG19, DenseNet121, and EfficientNetB0, and examined the preprocessing effect. The classification phase assessed the EfficientNetB0 model's efficiency on diverse datasets by batch size and epoch. The contributions of our work are outlined as follows:  
1. We proposed an integrated model that combines U-Net with ResNet34 for robust segmentation and EfficientNet-B0 for precise classification. The key innovation in our study lies in the integration of U-Net with ResNet34 to enhance segmentation accuracy and the use of EfficientNet-B0 for precise classification, a novel application specifically tailored for glaucoma detection. While U-Net has been widely used for medical image segmentation and combined with various backbones in other domains such as tumor detection and organ segmentation, our approach uniquely addresses the specific challenges of glaucoma detection with this combination. 2.