# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion
### Computational Efficiency Analysis  
Figure 11 shows the computational cost in comparison to the performance of individual models. **VGG19** demonstrates exceptional efficiency (\(\sim\)0.011), achieving the best ratio of accuracy to computational cost despite being a larger model. This unexpected result challenges the conventional wisdom about VGG's inefficiency. **EfficientNet** ranks second  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Architecture** & **Confidence** & **Confidence** & **Mean** & **Max** & **Difficult** \\  & **Mean** & **Std** & **Entropy** & **Entropy** & **Cases** \\ \hline ViT & 0.998 & 0.035 & 0.004 & 1.024 & 91 \\ CNN & 0.979 & 0.108 & 0.054 & 1.545 & 91 \\ DenseNet & 0.890 & 0.191 & 0.350 & 1.582 & 91 \\ MobileNet & 0.877 & 0.211 & 0.369 & 1.578 & 91 \\ VGG19 & 0.806 & 0.241 & 0.561 & 1.580 & 91 \\ ResNet & 0.522 & 0.365 & 0.713 & 1.585 & 91 \\ EfficientNet & 0.399 & 0.001 & 1.533 & 1.538 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Model interpretability metrics. Here, ViT, VGG, ResNet, and CNN stand for Vision Transformer, Visual Geometry Group, Residual Network, Convolutional Neural Network, respectively.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Architecture** & **Overlap Coefficient** & **Confusion Rate** & **Confidence Gap** \\ \hline ViT & 0.9286 & 0.0065 & 0.0038 \\ CNN & 0.8958 & 0.0159 & 0.0087 \\ VGG19 & 0.8222 & 0.0808 & 0.0616 \\ DenseNet & 0.8191 & 0.0469 & 0.0128 \\ MobileNet & 0.8323 & 0.0418 & 0.0281 \\ ResNet & 0.5673 & 0.2270 & 0.0312 \\ EfficientNet & 0.0000 & 0.5000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 11: MCI to AD boundary metrics. Here, ViT, VGG, ResNet, and CNN stand for Vision Transformer, Visual Geometry Group, Residual Network, Convolutional Neural Network, respectively.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Architecture** & **Interpretability** & **Visualization** & **Biomarker** & **Clinical** \\  & **Score** & **Approach** & **Alignment** & **Explainability** \\ \hline CNN & High & Gradient-based & High & High \\ ResNet & High & Gradient-based & High & High \\ VGG19 & High & Gradient-based & High & High \\ DenseNet & High & Gradient-based & High & Medium \\ EfficientNet & Medium–High & Efficient feature & High & Medium \\ MobileNet & Medium–High & Efficient feature & High & Medium \\ ViT & Medium & Attention-based & Medium & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 12: Model interpretability and clinical relevance. Here, ViT, VGG, ResNet, and CNN stand for Vision Transformer, Visual Geometry Group, Residual Network, Convolutional Neural Network, respectively.  
in efficiency (\(\sim\)0.0065), living up to its name as designed for efficiency through compound scaling of network dimensions. **ResNet** follows with moderate efficiency (\(\sim\)0.0048). **CNN** and **ViT** show similar mid-range efficiency scores (\(\sim\)0.0022 and \(\sim\)0.0026, respectively), despite representing completely different architectural approaches (traditional convolutional vs. Transformer-based). **DenseNet** and **MobileNet** surprisingly demonstrate the lowest efficiency scores (\(\sim\)0.0014 and \(\sim\)0.0012), with MobileNet being particularly disappointing given its design intent for mobile/resource-constrained environments.