## 1 Introduction  
Diabetic retinopathy and glaucoma are eye problems that can cause loss of vision unknowingly. More than 150 million people around the world are affected by these problems [1]. Glaucoma is characterized by progressive damage to the blood vessels and optic nerves in the eye, often associated with high intraocular pressure (IOP). Increased IOP eventually damages the optic nerve fibers, leading to a thickening of the retinal nerve fiber layer (RNFL) and a change in the cup-to-disc ratio (CDR). As glaucoma's impact progresses, the optic cup (the center of the optic nerve (ONH)) may become larger while the optic disc (the entire optic nerve) remains relatively stable in size. This region of interest (ROI) can be identified as the area where these changes occur. Figure 1 shows a visual representation of the differences in the cup-to-disc size, highlighted in blue, of a healthy and an affected eye, respectively, as observed in the Drishti-GS dataset. If left untreated, it can lead to extensive damage to the optic nerves, resulting in loss of vision. The biggest challenge in detecting these eye diseases is the absence of outward symptoms until vision loss has already occurred [2]. There are no warning signs in the early stages. Glaucoma is second to cataracts among the common causes of vision loss. By 2040, 111.8 million people between the ages of 40 and 80 will be diagnosed with glaucoma [3]. The key to preventing glaucoma is regular eye examinations after 40 years for early detection [4]. The earlier glaucoma is diagnosed and treated, the higher the patient's chances of preserving the vision of the affected eye. The assessment of fundus images of the retina taken with fundus cameras can be a valuable method of detecting glaucoma. The optic disc appears as the brightest yellow region on the fundus image. As mentioned earlier, changes in the shape of the cup region can serve as an early indicator of glaucoma. Traditionally, ophthalmologists manually examined these retinal fundus images to detect signs of glaucoma disease by assessing the ONH. However, this process was tedious and time-consuming. The automatic optic disc detection and glaucoma classification system offers ophthalmologists several advantages, including improving the diagnostic process and optimizing therapy. These systems contribute to a more efficient management of glaucoma and improve patient care. Many computer-aided diagnostic techniques are being proposed to analyze OCT images and aid the detection of various eye diseases. However, accurate optic disc (OD) segmentation is challenging due to factors such as poor contrast, low resolution, varying illumination, and noise in fundus images. Furthermore, selecting the best image features and classifiers for glaucoma detection is a significant challenge. Because it requires analyzing features from both the retinal image and the optic disc to achieve accurate segmentation and glaucoma detection. Since glaucoma disease is closely related to the optic disc nerve areas, ROI can be used for glaucoma assessment. Some computer-aided diagnosis algorithms focus solely on the segmentation of the optic discs, while others combine segmentation for ROI detection and subsequent classification. Two different approaches have been deployed in this study. The first approach for the segmentation of the optic disc utilizes the Grey Wolf Optimizer (GWO) algorithm. The second approach is classification, where we deploy both two-stage and one-stage approaches. In the one-stage approach, we directly use the whole image for training and testing. However, in the two-stage approach, we utilize the YOLO algorithm to segment the region of the optic disc and crop it. Then, we use the ROI and conduct model training and testing on the cropped ROI fundus image. To perform glaucoma classification for both cropped and uncropped images, we apply deep learning techniques, including convolutional neural networks (CNNs), vision transformers, specifically the Swin Transformer (ST), and a combination of CNNs with Random Forest (RF).