## 2 Research Methodology
### Results
However, ViT could have better generalization, reducing the bias in the identification of anomalies introduced by one or more different techniques to introduce anomalies. [PERSON] et al. [17] aimed to compare their ViT model with CNN networks and with another ViT model to perform image classification to detect skin cancer. The experiment conducted by the authors used a public HAM10000 dataset with dermatoscopic skin cancer images and a clinical dataset collected through dermoscopy. In this study, a multi-scale image and the overlapping sliding window were used to serialize the images. They also used contrastive learning to improve the similarity of different labels and minimize the similarity in the same label. Thus, the ViT model developed was better for skin cancer classification using these techniques. However, the authors also demonstrated the effectiveness of balancing the dataset on the model performance, but they did not present the F1-Score values before the dataset is balanced to verify the improvement. The authors in [19] aimed to study if ViT models could be an alternative to CNNs in time-critical applications. That is, for edge computing instances and IoT networks, applications using deep learning models consume multiple computational resources. The experiment used pre-trained networks such as ResNet152, DenseNet201, InceptionV3, and SL-ViT with three different datasets in the scope of images, audio, and video. They concluded that the ViT model introduced less overhead and performed better than the architectures used. It was also shown that increasing the kernel size of convolutional layers and using dilated convolutional caused a reduction in the accuracy of a CNN network. In a study carried out in [20], the authors tried to find in ViTs an alternative solution to CNN networks for asphalt and concrete crack detection. The authors concluded that ViTs, due to the self-attention mechanism, had better performance in crack detection images with intense noise. CNN networks in the same images suffered from a high number of false negative rates, as well as the presence of biases in image classification. [PERSON] in [16] aimed to analyse eight different Vision Transformers and compare them with the performance of a pre-trained CNN network and without the pre-trained parameters to perform traffic signal recognition in autonomous driving systems. In this study, three different datasets with images of real-world traffic signals were used. This allowed the authors to conclude that the pre-trained DenseNet161 network had a higher accuracy than the ViT models to do traffic sign recognition. However, it was found in this work that ViT models performed better than the DenseNet161 network without the pre-trained parameters. From this work, it was also possible to conclude that the ViT models with a total number of parameters equal to or greater than the CNN networks, used during the experiment, had a shorter training time. The work done in [13] compared CNN networks with Vision Transformers models for the classification of Diabetic Foot Ulcer images. For the study, the authors decided to use the following architectures: Big Image Transfer (BiT), EfficientNet, ViT-base and Data-efficient Image Transformers (DeIT) upon a dataset composed of 15,683 images. A further aim of this study to compare the performance of deep learning models using Stochastic Gradient Descent (SGD) [22] with Sharpness-Aware Optimization (SAM) [23; 24]. These two tools are optimizers that seek to minimize the value of the loss function, improving the generalization ability of the model. However, SAM minimizes the value of the loss function and the sharpness loss, looking for parameters in the neighbourhood with a low loss. Therefore, this work concluded that the SAM optimizer originated an improvement in the values of F1-Score, AUC, Recall and Precision in all the architectures used. However, the authors did not present the training and test values that allow for evaluating the improvement in the generalization of the models.