## 1 Introduction  
Glaucoma is a disease that damages the patient's optic nerve and is the leading cause of irreversible blindness worldwide [1]. A patient with glaucoma may remain asymptomatic until the disease reaches very advanced stages of development, making early diagnosis difficult. For the same reason, the number of people affected is assumed to be much higher than the number of diagnosed patients [2]. The optic nerve head, or \"optic disc\", is a structure located in the retina made up of various tissues (neural, vascular, and connective). Inside it is a depression called the \"optic cup\" (Figure 1). Glaucoma damages the optic nerve tissues, causing molecular and functional changes in this region. This results in alterations of the microcirculation, atrophy of nerve neurons, and an enlargement of the cup, leading to visual loss [2]. All these changes can be seen in fundus imaging or retinography, which makes this type of imaging useful for the diagnosis of glaucoma [3]. The visual study of these images is a very subjective and not always simple operation, especially in the most incipient cases of the disease. For this reason, automated methods such as Deep Learning (DL) algorithms can reduce costs and make fast and consistent predictions, helping the specialist in the diagnosis. In recent years, these DL algorithms have had a high impact on medicine. When the problem being addressed involves image processing and classification, it is quite common to use Convolutional Neural Networks (CNNs). These networks are able to automatically extract the most relevant features of the image and take advantage of the available spatial information. For this purpose, they apply convolutional filters (kernels) to the images [4]. Each of these filters is adjusted during the training of the network to detect a particular feature on the image so that the first layers of the network detect simple features (edges, textures, etc. ), and as it goes deeper, the filters of the last layers are able to recognise more complex features [5]. In medicine, the application of CNNs can be found in several fields: skin cancer [6], lung diseases [7], heart diseases [8], breast cancer [9], vascular diseases [10], etc. In the case of Ophthalmology, these CNNs have been widely used for the diagnosis of diabetic retinopathy [11], macular degeneration [12], cataracts [13], and glaucoma [14; 15; 16; 17]. In relation to glaucoma, the performance of three well-known CNNs is studied in [14]. The three best-fit models have very similar performance, offering accuracy values around 98% for a particular dataset. Similarly, in [16], the authors study the efficiency of ten well-known CNNs, obtaining a balanced accuracy of 87.48% with the VGG19 architecture [18]. A comparative study of the efficiency of three well-known CNNs and an ensemble model that combines the individual predictions of these three models is presented in [15]. In the training stage of these systems, several experiments are performed using four separate public and private datasets and, finally, with all these datasets fused together. The ensemble model proves to be superior in all tests, with accuracy values in the range of 88-98%. In [17], a new CNN architecture is proposed, trained, and tested with two different retinography databases, and the efficiency achieved in both cases is compared, which is around 0.831 and 0.887 (Area Under de Curve--AUC values). On the other hand, Transformers [19], a DL architecture initially designed for Natural Language Processing (NLP), have recently appeared. Their results in NLP have been spectacular, which has sparked interest in adapting them for image processing. These Transformers adapted to work with images are known as Vision Transformers (ViTs) [20; 21; 22]. Broadly speaking, the Transformers apply a mechanism called \"attention\", which allows the model to focus on the most relevant parts of the input sentence, assigning each word a weight proportional to its importance.