## 2 Research Methodology
### Results
The authors in [7] studied the performance of ViT in comparison with other architectures to detect pneumonia, through chest X-ray images. Therefore, a ViT model, a CNN network developed by the authors and the VGG-16 network were used for the study which focussed on a dataset with 5856 images. After the experiments performed, the authors concluded that ViT was better than CNN with 96.45% accuracy, 86.38% validation accuracy, 10.8% loss and 18.25% validation loss. In this work, it was highlighted that ViT has a self-attention mechanism that allows splitting the image into small patches that are trainable, and each part of the image can be given an importance. However, the attention mechanism as opposed to the convolutional layers makes ViT's performance saturate fast when the goal is scalability. In the study [21] the goal was to compare ViT with state-of-art CNN networks to classify UAV images to monitor crops and weeds. The authors compared the influence of the size of the training dataset on the performance of the architectures and found that ViT performed better with fewer images than CNN networks in terms of F1-Score. They concluded that ViT-B/16 was the best model to do crop and weed monitoring. In comparison with CNN networks, ViT could better learn the patterns of images in small datasets due to the self-attention mechanism. In the scope of lung diseases, the authors in [11] investigated the performance of ViT models to automatically classify emphysema subtypes through Computed Tomography (CT) images in comparison with CNN networks. In this study, they performed a comparative study between the two architectures using a dataset collected by the authors (3192 patches) and a public dataset of 168 patches taken from 115 HRCT slides. In addition to this, they also verified the importance of pre-trained models. They concluded that ViT failed to generalize when trained with fewer images, because when comparing the pre-training accuracy with 91.27% on the training and 70.59% on the test. In the work in [9], a comparison between state-of-the-art CNNs and ViT models for Breast ultrasound image classification was developed. The study was performed with two different datasets: the first containing 780 images and the second containing 163 images. The following architectures were selected for the study: ResNet50, VGG-16, Inception, NASNET, ViT-S/32, ViT-B/32, ViT-Ti/16, R + ViT-Ti/16 and R26 + ViT-S/16. ViT models were found to perform better than CNN networks for image classification. The authors also highlighted that ViT models could perform better when they were trained with a small dataset, because via the attention mechanism, it was possible to collect more information from different patches, instead of collecting information from the image. [PERSON] et al., in [5], compared ViT models, with the MLP-Mixer architecture and with CNNs. The goal was to evaluate which architecture was more robust in image classification. The study consisted of generating perturbations and adverse examples in the images and understanding which of the architectures was most robust. However, this study did not aim to analyse the causes. Therefore, the authors concluded that ViT were more robust than CNNs to adversarial attacks and from a features perspective CNN networks were more sensitive to high-frequency features. It was also described that the shift-variance property of convolutional layers may be at the origin of the lack of robustness of the network in the classification of images that have been transformed. The authors in [15] performed an analysis between ViT and CNN models aimed at detecting deepfake images. The experiment consisted in using the ForgeryNet dataset with 2.9 million images and 220 thousand video clips, together with three different image manipulation techniques, where they tried to train the models with real and manipulated images. By training the ViT-B model and the EfficientNetV2 network the authors demonstrated that the CNN network could generalize better and obtain higher training accuracy.