## 3 Experiments and Results
### Classification Performance  
The classification performance of the introduced model settings was reported and analyzed. The model was considered as follows: accuracy, recall, precision, and F1 score [40]. Depending on the normal and abnormal outcomes of the model, it can be represented as true positive (TP, the total number of correct predictions in the abnormal case), true negative (TN, the total number of incorrect predictions in the abnormal case), false positive (FN, the total number of correct predictions in the normal case), and false negative (FN, the total number of incorrect predictions in the normal case).  
Accuracy is a performance metric that is typically evaluated when positive and negative groups are equal.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
Recall (=sensitivity) is the percentage of correctly predicted positive observations out of the total positive predictions.  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
Precision calculates the accuracy of the classification model with a positive predicted value.  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
Accuracy does not take into account the distribution of the data. The F1 score is used to manage the distribution.  
\[F1\ score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{11}\]  
In Table 4, we confirm that the ViT model performed better than the VGG19 model for the classification of normal and abnormal subjects. However, contrary to the expectations, we observe that the augmented dataset exhibited worse results than the original dataset irrespective of the model. This suggests that the augmented data applied to the two models were not effective. When the augmented dataset was used, recall and precision were very low, indicating that there were many false positives. We believe that the model was trained to match the abnormal group, as the quantity of abnormal data increased proportionally when data augmentation was performed with a 1:2 ratio between the normal and abnormal groups. The confusion matrix for the binary classification in Table 4 can be found in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Data Set** & **ACC** & **Recall** & **Precision** & **F1 Score** \\ \hline ViT & original data & 0.8000 & 0.6000 & 0.7500 & 0.6667 \\ ViT & augmented data & 0.7000 & 0.1000 & 1.0000 & 0.1818 \\ VGG19 & original data & 0.7333 & 0.6000 & 0.6000 & 0.6000 \\ VGG19 & augmented data & 0.6667 & 0.1000 & 0.5000 & 0.1667 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Binary classification performance of ViT and VGG19.  
In Table 6, VGG19 shows better classification performance for AD, MCI, and HC with the original dataset, whereas ViT shows better classification performance with the augmented data. However, both models generally exhibited a low classification performance of less than 0.7. The confusion matrix for the three-class classification in Table 6 can be found in Table 7. As shown in Table 4, classification performance with the augmented dataset is better than that with the original dataset.  
Because performance with the augmented dataset is poorer than that with the original dataset, we attempted to check the classification performance in training and validation. As shown in Table 8, the validation accuracy of the ViT and VGG19 models was higher with the augmented dataset than the original dataset. In the binary case, the validation accuracy of VGG19 was higher than that of ViT, and in the three-class case, ViT and VGG19 both exhibited higher validation accuracy.