# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion  
Table 1 shows classification performance of the base models, namely, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet, a custom-built multi-classifier CNN, and ViT for Alzheimer's disease classification. The ViT model achieved the highest accuracy of 98.0%, with precision and F1-scores also at 98.0% and a recall of 97.0%. Its Transformer-based architecture, known for modeling long-range dependencies and spatial hierarchies in image data, likely contributed to its ability to effectively capture complex patterns in the dataset. The custom-built multi-classification CNN demonstrated an accuracy of 94.0%, with precision, recall, and F1-scores all at 94.0%, indicating consistent performance across these metrics. Despite a lack of advanced feature extraction mechanisms, the CNN-based model remains competitive in Alzheimer's disease classification. DenseNet achieved an accuracy of 88.0% with a precision of 89.0%, recall of 87.0%, and F1-score of 88.0%. Its densely connected architecture likely facilitated feature reuse, which may explain its relatively strong performance. MobileNet, with an accuracy of 87.0%, demonstrated precision, recall, and F1-scores of 87.0%, showing comparable effectiveness to DenseNet in this application. The VGG-19 model achieved an accuracy of 83.0%, with precision and F1-score values of 84.0% and a recall of 83.0%, while its performance is satisfactory, it was outperformed by models such as DenseNet and the custom CNN, likely due to its simpler architecture. ResNet demonstrated an accuracy of 65.0%, precision of 78.0%, recall of 65.0%, and F1-score of 66.0%. This relatively low performance indicates difficulties in capturing relevant features for this classification task. Adjustments to its architecture or further optimization may improve its performance. EfficientNet exhibited the lowest performance, with an accuracy of 41.0%, precision of 16.0%, recall of 41.0%, and an F1-score of 23.0%. This result suggests a potential mismatch between the model's scale and complexity and the dataset characteristics, leading to suboptimal outcomes in Alzheimer's disease classification. Figure 4 reveals distinct performance patterns across the neural network architectures, with ViT and standard CNNs demonstrating exceptional performance, consistently approaching the 95% reference threshold across all metrics. Mid-tier models, namely, VGG-19, MobileNet, and DenseNet, maintain stable 80-90% performance with minimal variance. ResNet presents an interesting case, with precision significantly outperforming its other metrics by approximately 10 percentage points. Most notably, EfficientNet exhibits substantial limitations despite its design focus, particularly in F1-Score, where it achieves only about 23%, suggesting significant precision-recall tradeoffs. The tight confidence intervals \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
**ViT** & 98.0\% & 98.0\% & 97.0\% & 98.0\% \\ VGG-19 & 83.0\% & 84.0\% & 83.0\% & 84.0\% \\ ResNet & 65.0\% & 78.0\% & 65.0\% & 66.0\% \\ MobileNet & 87.0\% & 87.0\% & 87.0\% & 87.0\% \\ EfficientNet & 41.0\% & 16.0\% & 41.0\% & 23.0\% \\ DenseNet & 88.0\% & 89.0\% & 87.0\% & 88.0\% \\ CNN & 94.0\% & 94.0\% & 94.0\% & 94.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance metrics of base models and ViT for Alzheimer’s disease Classification. Here, ViT, VGG, ResNet, and CNN stand for Vision Transformer, Visual Geometry Group, Residual Network, Convolutional Neural Network, respectively. observed for high-performing models indicate statistical reliability and consistency across evaluation instances while revealing an apparent inverse relationship between model efficiency and classification performance in this specific application context.