## 3 Experiments
### Results  
Classification of AD vs. MCI: The first experiment is intended towards the testing and validation of the proposed method for the AD vs. MCI classification. The classification results are shown in Table 2 (AD vs. MCI column). The best results were obtained by the PCT feature sets with the proposed SaDE-WNN are 93.74% accuracy, 86.0% sensitivity, 98.0% specificity, and 88.5 F1. The best results for the RM feature sets are 92.0% accuracy, 84.1% sensitivity, 95.6% specificity, and 86.0 F1. The difference between accuracy, sensitivity, specificity, AUC between the PCT texture and the RM texture features are statistically significant (\(p\) < 0.05). However, differences are not statistically significant for PCT and PCT (\(p\) > 0.05).  
Classification of AD vs. HC: The classification results are shown in Table 2 (AD vs. HC column) in terms of accuracy, sensitivity, specificity for each feature set. The best results obtained by the PCT feature sets with the proposed SaDE-WNN are 94.4% accuracy, 88.7% sensitivity, 98.9% specificity, and 93.8 F1. The best results for the RM feature sets with SaDE-WNN are 93.3% accuracy, 87.6% sensitivity, 96.6% specificity, and 91.7 F1 scores. However, we did not notice any significant improvement in classification results. The difference between accuracy, sensitivity, specificity, between the PCT texture features and the RM texture features are statistically significant, but these differences are not statistically significant for combined and ZM (\(p\) > 0.05).  
Classification of MCI vs. HC: In this experiment, the proposed method is tested and validated for the MCI vs. HC classification. The classification results are shown in Table 2 (MCI vs. HC column). The best of accuracy, sensitivity, specificity, and F1 for the PCT with SaDE-WNN are 92.9%, 95.2%, 88.9%, and 93.2%. The overall performance of the proposed  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extraction Methods**} & \multirow{2}{*}{**Classifiers**} & \multicolumn{4}{c}{**AD vs. MCI**} & \multicolumn{4}{c}{**AD vs. HC**} & \multicolumn{4}{c}{**MCI vs. HC**} \\ \cline{2-13}  & & _Sens._ & _Spec._ & _Acc._ & _F1_ & _Sens._ & _Spec._ & _Acc._ & _F1_ & _Sens._ & _Spec._ & _Acc._ & **F1** \\ \hline RM & & 70.2 & 96.5 & 82.7 & 81.0 & 83.7 & 88.8 & 90.7 & 89.2 & 84.9 & 87.9 & 82.5 & 88.7 \\ ZM & & 72.0 & 94.9 & 87.8 & 82.8 & 85.5 & 92.3 & 92.7 & 90.4 & 92.5 & 92.8 & 87.2 & 89.7 \\ PCET & & 83.0 & 95.2 & 91.6 & 88.6 & 86.9 & 94.2 & 91.9 & 90.2 & 93.4 & 83.5 & 91.0 & 89.9 \\ PCT & & 84.1 & 95.6 & 92.0 & 86.0 & 87.6 & 96.6 & 93.3 & 91.7 & 93.5 & 86.1 & 92.5 & 91.3 \\ \hline RM & & 70.5 & 96.9 & 83.2 & 81.2 & 85.5 & 92.5 & 89.9 & 83.2 & 88.6 & 87.3 & 83.8 & 89.9 \\ ZM & & 72.8 & 98.2 & 89.4 & 85.3 & 86.7 & 94.8 & 92.1 & 91.1 & 92.5 & 90.6 & 90.5 & 90.1 \\ PCET & & 80.9 & 97.9 & 91.8 & 86.7 & 86.9 & 95.5 & 92.7 & 92.7 & 93.9 & 84.8 & 91.7 & 91.7 \\ PCT & & 85.9 & 97.2 & 92.7 & 87.2 & 87.7 & 97.3 & 93.0 & 92.5 & 94.7 & 87.7 & 92.5 & 92.0 \\ \hline RM & & 70.7 & 97.5 & 85.7 & 83.7 & 83.3 & 95.6 & 90.8 & 91.0 & 89.0 & 88.6 & 88.8 & 87.8 \\ ZM & & 72.3 & 99.7 & 91.2 & 85.8 & 85.0 & 96.7 & 92.1 & 91.2 & 88.0 & 93.9 & 90.7 & 89.5 \\ PCET & & 81.7 & 95.9 & 93.0 & 87.0 & 93.1 & 93.5 & 93.4 & 93.2 & 96.1 & 85.1 & 91.6 & 91.4 \\ PCT & & 86.0 & 98.0 & 93.7 & 88.5 & 88.7 & 98.9 & 94.4 & 93.8 & 95.2 & 88.9 & 92.9 & 93.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results for comparative analysis of the Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN) with Differentially optimized WNN (DEWNN) and Ada-DEWNN. Results are compared on the features extracted by non-orthogonal moments (RM) and orthogonal moments (ZM, PCET, and PCT).  
method for the RM feature sets with SaDE-WNN is 92.5% accuracy, 93.5% sensitivity, 86.1% specificity, and 91.3 F1. Again, the results are statistically significant for the RM texture features and PCT. However, as expected, there is no statistical significance is shown between the PCT, PCET, and PST texture feature sets.