## 3 Results  
Using the data split as described previously, the proposed model was trained with 77 subjects and tested with 19 subjects. Table 3 summarizes the model's performance at the image and subject levels. A training accuracy of 88.1% at image level and 83.6% at subject level was observed. Overall, an accuracy of 87.7% at image level and an accuracy of 80.2% at subject level were achieved. The performance of the model for individual groups are also tabulated in Table 3. In the case of dementia, the model achieved an accuracy of 91.7% at both image and subject level. In case of MCI, we obtained an accuracy of 83.0% at image level and 80.9% at subject level. For healthy subjects, we observed an accuracy of 92.3% at image level and 75.7% at subject level. [PERSON]'s correlation coefficient between the estimated rating and ground truths at both image level and subject level were evaluated and are as shown in Figure 3. A correlation coefficient of 0.9169 (\(p\)-value < 0.0001) at subject level and 0.9335 (\(p\)-value < 0.0001) at image level shows the reliability of the estimated values. Subject wise prediction of all subjects considered in this study are also analyzed and the results are as shown in Figure 4. In Figure 4, the estimated rating score of each subject, along with the ground truth, is presented. Figure 4 depicts all subjects who were correctly predicted and Figure 4 depicts all subjects who were incorrectly predicted. In the case of correctly predicted subjects, the prediction was correct despite the differences presented in the image-wise predictions. In the case of incorrectly predicted subjects, the prediction scores of most of the subjects were lower than the manual score (ground truth). This is because the numbers of EPVS were close to the critical numbers of the decision boundaries (for instance, when the number of EPVS is 11 or 21, the model tends to estimate the EPVS rating score as 1 or 2 instead of 2 or 3). Figure 3: [PERSON]’s correlation between the predictions and the ground truth rating scales at image level (**left**) and at subject level (**right**) (at image level, all points (Gi, Pi) \(\forall\) i = 1:576 and at subject level, all points (Gs, Ps) \(\forall\) s = 1:96 were shown in the figure where G is the ground truth and P is the prediction. [PERSON]’s correlation coefficient of 0.9169 (\(p\)-value < 0.0001) at subject level and 0.9335 (\(p\)-value < 0.0001) at image level shows the reliability of the model estimation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{4}{c}{**Whole Data**} & \multicolumn{4}{c}{**For Individual Groups**} \\ \cline{2-7}  & **Loss** & **Training** & **Testing** & **Dementia** & **MCI** & **Healthy** \\ \hline \multirow{2}{*}{Image level} & Cross-entropy & 88.1 \(\pm\) 0.6 & 87.7 \(\pm\) 1.6 & 91.7 & 83.0 & 92.3 \\  & MSE & 87.1 \(\pm\) 0.9 & 87.6 \(\pm\) 2.7 & 88.7 & 83.1 & 90.3 \\ \hline \multirow{2}{*}{Subject level} & Cross-entropy & 83.6 \(\pm\) 1.5 & 80.3 \(\pm\) 6.6 & 91.7 & 80.9 & 75.7 \\  & MSE & 82.9 \(\pm\) 2.9 & 80.1 \(\pm\) 4.5 & 87.9 & 78.9 & 76.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of EPVS rating estimation model on whole data and for individual groups (testing and training accuracy is reported as mean \(\pm\) standard deviation). Table 4 summarizes the findings of an ablation study (an experiment with and without the pre-processing step). In pre-processing, the Haar transformation-based approach was applied to enhance the EPVS in MRI images. When the pre-processing step is included, estimation performances are increased by 10% at the subject level and 11% at the image level. This experiment result demonstrates that pre-processing for EPVS enhancement results in a more robust estimation model.