## 4 Discussion
In [14], the authors use several retinography datasets to train and test three different types of CNNs (ResNet101, NasNet, and NasNet_large). None of these networks has been studied in our work, and the only dataset we have used in common is Drishti-GS1. Their best classification results on this dataset (accuracy 77.23-82.18%) are worse than those obtained by the CNN architectures used in our study (accuracy 79.21-88.12%). A comparative study between three CNNs (GoogleNet, VGG, and ResNet50) and an ensemble model of these architectures is presented in [15]. The datasets used are, again, different from those used by us, except for Drishti-GS1. With the two CNN architectures we have in common, VGG and ResNet50, the authors of [15] achieve high accuracy values of 91.08% and 93.06%, respectively. In our experiments with this dataset, the best VGG19 model presented an accuracy value of 84.16%, and the best ResNet50 model presented an accuracy value of 88.12%. Our values are lower, but we have not included retinographies of this dataset in our training, as in [15]. In [16], five different CNN architectures are trained using the Rim-ONE base for fine-tuning and the ESPERANZA and Drishti-GS1 datasets for deep tuning and the subsequent test process. Of the five architectures, two are analysed in our work: VGG19 and ResNet50. The best-balanced accuracy value obtained in [16] with VGG19 is 87.48%, and with ResNet50, it is 85.08%. These values are very similar to those obtained by us in the classification of the Drishti-GS1 dataset: 85.14% with ResNet50 and 82.47% with VGG19. A comparison between different CNNs (VGG19, VGG16, Xception, ResNet50, ConvNext) and ViT-based (original ViT, Swin Transformer) architectures is presented in [36]. The authors merge the Drishti-GS1, Refuge, and Rim-ONE datasets to train and test the models. The best accuracy values they found are the following: 93.2% with VGG19, 91.8% with Xception, 91.1% with ResNet50, 95.8% with ViT, and 91.8% with Swin Transformer. It can be observed that there are no very significant differences in the performance of CNNs and ViT-based systems, which is something that we have also found in our experiments. The results presented in [36] are similar to those obtained in our experiments with the test set. With Refuge, the results of the CNNs in [36] are slightly higher than those obtained by our CNNs, and the accuracy values of the ViT-based systems remain similar. With Drishti-GS1, our results are noticeably worse than those found in [36] for all models. This difference between accuracy values is logical since, in our study, the Refuge and Drishti-GS1 datasets have been used exclusively for testing, unlike [36], which uses them both to train and test the models. In [35], a comparative study of the performance of eight architectures based on ViTs is presented, among which we highlight five that coincide with those analysed in our work: Swin Transformer, CaiT, DeiT, original ViT, and ResMLP. To train and test all the models, the authors merge nine different public and private datasets so that they manage to reach a very high number of samples for training. Therefore, they achieved very high accuracy values: 93.2% with Swin Transformer, 94.5% with CaiT, 88.0% with DeiT, 87.4% with original ViT, and 91.5% with ResMLP. It is difficult to compare these results with ours since our training set is much smaller. Even so, we find it important to highlight that the accuracy values presented in Table 1 (test set classification results) are similar and even higher in some cases. Finally, in [39], ResNet50 is compared with DeiT. The experimental methodology followed in this work is similar to ours: the architectures are trained with a single dataset (OHTS) and tested with other different datasets (DIGS/ADAGES, ACRIMA, LAG, RimONE, ORIGA). The authors found that the DeiT architecture generalises better than ResNet50 in all the experiments. However, in our study, ResNet50 is superior to DeiT in almost all experiments.