## 4 Discussion  
Authors should discuss the results and how they can be interpreted from the previous perspective because the idea that ViT conveys the function of different regions within the brain has great potential for future work. The model's performance was compared with that of the CNN-based model VGG19. Although ViT exhibited higher performance than VGG19 in the binary classification task, its performance was low in the three-class classification. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Data Set**} & \multirow{2}{*}{**ACC**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1 Score**} \\ \cline{3-3} \cline{5-10}  & & & & & & & & & \\ \hline ViT & original data & 0.5667 & 0.5667 & 0.5278 & 0.5455 \\ ViT & augmented data & 0.5333 & 0.5333 & 0.5056 & 0.5174 \\ VGG19 & original data & 0.6667 & 0.6667 & 0.6794 & 0.6660 \\ VGG19 & augmented data & 0.4667 & 0.4667 & 0.3286 & 0.3673 \\ \hline \hline \end{tabular} Three-class: AD vs. MCI vs. HC, ViT: Vision Transformer, ACC: accuracy.  
\end{table} 
Table 6: Three-class classification performance of ViT and VGG19. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{6}{c}{**2 Class**} & \multicolumn{6}{c}{**3 Class**} \\ \cline{3-10}  & & **ViT** & **VGG19** & **ViT** & **VGG19** \\ \hline original data & 0.6893 & 0.8119 & 0.6429 & 0.6429 \\ augmented data & 0.7689 & 0.9671 & 0.7778 & 0.8260 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Three-class classification performance of ViT and VGG19. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Data Set**} & \multirow{2}{*}{**ACC**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1 Score**} \\ \cline{3-3} \cline{5-10}  & & & & & & & & & & \\ \hline ViT & original data & 0.5667 & 0.5667 & 0.5278 & 0.5455 \\ ViT & augmented data & 0.5333 & 0.5333 & 0.5056 & 0.5174 \\ VGG19 & original data & 0.6667 & 0.6667 & 0.6794 & 0.6660 \\ VGG19 & augmented data & 0.4667 & 0.4667 & 0.3286 & 0.3673 \\ \hline \hline \end{tabular} Three-class: AD vs. MCI vs. HC, ViT: Vision Transformer, ACC: accuracy.  
\end{table} 
Table 7: Three-class classification confusion matrix of ViT and VGG19. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{6}{c}{**2 Class**} & \multicolumn{6}{c}{**3 Class**} \\ \cline{3-10}  & & **ViT** & **VGG19** & **ViT** & **VGG19** \\ \hline original data & 0.6893 & 0.8119 & 0.6429 & 0.6429 \\ augmented data & 0.7689 & 0.9671 & 0.7778 & 0.8260 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Three-class classification performance of ViT and VGG19. Furthermore, when data augmentation was applied, classification performance was lower than that of the original data regardless of the model. When comparing the validation accuracy performance following training, a higher value was obtained in the case of the augmented data; therefore, data augmentation resulted in overfitting. There are several speculations regarding the failure of ViT. The first is the augmented data problem. In fact, data augmentation resulted in lower classification performance regardless of the model. We attempted data augmentation through image rotation to solve the problem of data scarcity. Consequently, the training set increased in volume from 60 subjects to 630 subjects. When the actual augmented data were applied, the validation accuracy after training increased. However, when performance was compared by applying the test, it was lower than that before data augmentation. Furthermore, it was confirmed that the classification performance for normal subjects deteriorated as the data were augmented. Furthermore, we did not perform proper fine-tuning. Although ViT consumes relatively less time per epoch than VGG19, it requires significant trial and error to optimize the hyperparameters.