# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 4 Results
### Performance Assessment Related to Classification  
For the classification phase, the proposed MGTO-CapsNet model's performance was compared with that of six different classification models (CNN, GoogleNet, VGG-16, AlexNet, ResNet-50, and Deep Belief Network (DBN)) with the MGTO algorithm on the ADNI, real-time, and AIBL datasets. Specifically, as seen in Table 2, the CapsNet model with the MGTO algorithm obtained efficient classification results with 99.94% for specificity, 99.96% for the phi coefficient, 99.88% for accuracy, 99.90% for fall-out, and 99.80% for sensitivity on the ADNI dataset. These evaluation measures were significantly higher than  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**ADNI Dataset**} & \multicolumn{3}{c}{**Real-Time Dataset**} & \multicolumn{3}{c}{**AIBL Dataset**} \\ \cline{2-10}  & **PA** & **JSC** & **DSC** & **PA** & **JSC** & **DSC** & **PA** & **JSC** & **DSC** \\ \hline Watershed & 0.76 & 0.84 & 0.86 & 0.68 & 0.70 & 0.76 & 0.80 & 0.76 & 0.84 \\ Region growing & 0.78 & 0.85 & 0.90 & 0.77 & 0.75 & 0.82 & 0.86 & 0.79 & 0.88 \\ Adaptive thresholding & 0.80 & 0.88 & 0.91 & 0.85 & 0.78 & 0.88 & 0.89 & 0.82 & 0.89 \\ Superpixel clustering & 0.83 & 0.90 & 0.92 & 0.87 & 0.86 & 0.89 & 0.90 & 0.85 & 0.90 \\ Otsu thresholding & 0.90 & 0.93 & 0.94 & 0.92 & 0.91 & 0.92 & 0.93 & 0.89 & 0.91 \\ Fuzzy-based superpixel clustering & 0.96 & 0.97 & 0.98 & 0.94 & 0.95 & 0.95 & 0.98 & 0.96 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance assessment of the state-of-the-art segmentation algorithms.  
those of the other conventional deep learning models such as CNN, GoogleNet, VGG-16, AlexNet, ResNet-50, and DBN.  
These conventional deep learning models were executed in a similar environment under the following parameters: the optimizer was Adam, the activation function was ReLU, the learning rate was 0.001, the batch size was 64, the loss function was cross-entropy, the dropout rate was 0.05, and the total epochs was 100. A pictorial comparison of the MGTO-CapsNet model and the conventional models on the ADNI dataset is presented in Figure 5. In the classification phase, the MGTO-CapsNet model and the conventional models were confirmed to have dissimilar K-fold techniques. The MGTO-CapsNet model and the conventional models obtained maximum classification results in the fivefold cross validation (20%:80% testing and training). The obtained results are depicted in Tables 2-4.  
Correspondingly, as seen in Tables 3 and 4, the combination of CapsNet with the MGTO algorithm had better results on the both the real-time and AIBL datasets. On the real-time dataset, the MGTO-CapsNet model obtained a 98.13% specificity, a 97.80% phi coefficient, a 96.38% accuracy, a 97.88% fall-out, and a 97.90% sensitivity. Similarly, on the AIBL dataset, the MGTO-CapsNet model achieved a 99.91% specificity, a 99.92% phi coefficient, a 99.94% accuracy, a 99.95% fall-out, and a 99.88% sensitivity. The pictorial comparison of the MGTO-CapsNet model and the conventional models on the real-time and AIBL datasets is represented in Figures 6 and 7. In this context, in comparison to other conventional deep learning models, the CapsNet model has better generalization ability