## 4 Discussion  
The results can be summarized as follows. In [12], ViTs were found to perform better and be more resilient to images with natural or adverse disturbances compared to CNNs. Another study [14] concluded that ViTs are more robust in digital holography because they can access the entire hologram rather than just a specific area, giving them an advantage. ViTs have also been found to outperform CNNs in detecting pneumonia in chest X-ray images [7] and in classifying UAV images for crop and weed monitoring with small datasets [21]. However, it has been noted that ViT performance may saturate if scalability is the goal [7]. In a study on classifying emphysema subtypes in CT images [11], ViTs were found to struggle with generalization when trained on fewer images. Nevertheless, ViTs were found to outperform CNNs in breast ultrasound image classification, especially with small datasets [9]. Another study [5] found that ViTs are more robust to adversarial attacks and that CNNs are more sensitive to high-frequency features. The authors in [15] found that CNNs had higher training accuracy and better generalization, but ViTs showed potential to reduce bias in anomaly detection. In [17], the authors claimed that the ViT model showed better performance for skin cancer classification. ViTs have also been shown to introduce less overhead and perform better for time-critical applications in edge computing and IoT networks [19]. In [20], the authors investigated the use of ViTs for asphalt and concrete crack detection and found that ViTs performed better due to the self-attention mechanism, especially in images with intense noise and biases. [PERSON] [16] found that a pre-trained CNN network had higher accuracy, but the ViT models performed better than the non-pre-trained CNN network and had a shorter training time. The authors in [13] used several models for diabetic duct ulcer image classification and compared SGD and SAM optimizers, concluding that the SAM optimizer improved several evaluation metrics. In [18], the authors showed that fine-tuning performed better on ViT models than CNNs for insect pest recognition. Therefore, based on the information gathered from the selected papers, we attempt to answer the research questions posed in Section 1:  
RQ1--Can the ViT architecture have a better performance than the CNN architecture, regardless of the characteristics of the dataset? The literature review shows that ViT in image processing can be more efficient in smaller datasets due to the increase of relations created between images through the self-attention mechanism. However, it is also shown that if ViT trained with little data will have less generalization ability and worse performance compared to CNN's. RQ2--What influences the CNNs that do not allow them to perform as well as the ViTs? Shift-invariance is a limitation of CNN that makes the same architecture not have a satisfactory performance because the introduction of noise in the input images makes the same architecture unable to get the maximum information from the central pixels. However, the authors in [27] propose the addition of an anti-aliasing filter which combines blurring with subsampling in the Convolutional, MaxPooling and AveragePooling layers. Demonstrating through the experiment carried out that the application of this filter originates a greater generalization capacity and an increase in the accuracy of CNN. Furthermore, increasing the kernel size in convolutional layers and using dilated convolution have been shown as limitations that deteriorate the performance of CNNs against ViTs. RQ3--How does the Multi-Head Attention mechanism, which is a key component of ViTs, influence the performance of these models in image classification? The Attention mechanism is described as the mapping of a query and a set of key-value pairs to an output, the output being the result of a weighted sum of the values, in which the weight given is calculated, through the query with the corresponding key by a compatibility function.