## 2 Research Methodology
### Results  
After applying the inclusion and exclusion criteria to the papers obtained in each of the electronic databases, seventeen (17) papers were selected for the literature review. Table 3 lists all the papers selected for this work, the year of publication and the type of publication. \begin{table}
\begin{tabular}{c c} \hline
**Data Source** & **Search String** \\ \hline ACM Digital Library & ((Vision Transformers) AND (convolutional neural networks) AND (images classification) AND (comparing)) \\ Google Scholar & ((ViT) AND (CNN) AND (Images Classification) OR (Comparing) OR (Vision Transformers) OR (convolutional neural networks) OR (differences)) \\ Science Direct & ((Vision Transformers) AND (convolutional neural networks) AND (images classification) AND (comparing)) \\ Scopus & ((ViT) AND (CNN) AND (comparing)) \\ Web of Science & ((ViT) AND (CNN) AND (comparing)) \\ \hline \end{tabular}
\end{table} 
Table 2: Data sources and used search string. \begin{table}
\begin{tabular}{c c c} \hline
**Ref.** & **Title** & **Type** \\ \hline
[5] & Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs & 2021 & Conference \\
[6] & Are Transformers More Robust Than CNNs? & 2021 & Conference \\
[7] & Detecting Pneumonia using Vision Transformer and comparing with other techniques & 2021 & Conference \\
[8] & Do Vision Transformers See Like Convolutional Neural Networks? & 2021 & Conference \\
[9] & Vision Transformer for Classification of Breast Ultrasound Images & 2021 & Conference \\
[10] & ConvNets vs. Transformers: Whose Visual Representations are More Transferable? & 2021 & Conference \\
[11] & A vision transformer for emphysema classification using CT images & 2021 & Journal \\
[12] & Comparing Vision Transformers and Convolutional Nets for Safety Critical Systems & 2022 & Conference \\
[13] & Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification & 2022 & Conference \\
[14] & Convolutional Neural Network (CNN) vs Vision Transformer (ViT) for Digital Holography & 2022 & Conference \\
[15] & Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection & 2022 & Conference \\
[16] & Traffic Sign Recognition with Vision Transformers & 2022 & Conference \\
[17] & An improved transformer network for skin cancer classification & 2022 & Journal \\
[18] & CNN and transformer framework for insect pest classification & 2022 & Journal \\
[19] & Single-layer Vision Transformers for more accurate early exits with less overhead & 2022 & Journal \\
[20] & Vision transformer-based autonomous crack detection on asphalt and concrete surfaces & 2022 & Journal \\
[21] & Vision Transformers for Weeds and Crops Classification of High-Resolution UAV Images & 2022 & Journal \\ \hline \end{tabular}
\end{table} 
Table 3: List of selected studies. Figure 3 shows the distribution of the selected quad  
Figure 4 shows the distribution of the selected quad  
Figure 3: Distribution of the selected quad
perturbations were applied to the dataset images. It was concluded that ViT can perform better and be more resilient on images with natural or adverse disturbances than CNN. It was also found in this work that the combination of the two architectures results in a 10% improvement in accuracy (Acc). The work done in [14] aimed to compare Vision Transformers (ViT) with Convolutional Neural Networks (CNN) for digital holography, where the goal was to reconstruct amplitude and phase by extracting the distance of the object from the hologram. In this work, DenseNet201, DenseNet169, EfficientNetB4, EfficientNetB7, ViT-B/16, ViT-B32 and ViT-L/16 architectures were compared with a total of 3400 images. They were divided into four datasets, original images with or without filters, and negative images with or without filters. The authors concluded that ViT despite having an accuracy like CNN, was more robust because, due to the self-attention mechanism, it can learn the entire hologram rather than a specific area.