# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion
### Performance Evaluation Using ROC and AUC Metrics
The overlap coefficient measures sensitivity to early biomarkers, the confusion rate indicates specificity, and the confidence gap reflects the model's certainty. ViT demonstrates an optimal balance between sensitivity (0.9423 overlap) and specificity (0.0017 confusion), while EfficientNet shows high sensitivity (0.9500) but poor specificity (0.5000), indicating limited discriminative ability. ResNet, despite moderate overall performance, exhibits the highest confidence gap (0.0143), suggesting that when it identifies biomarkers, it does so with greater certainty. Table 10 addresses model interpretability through confidence and uncertainty metrics. Higher confidence with lower standard deviation and lower entropy values generally indicate more interpretable models. ViT leads in interpretability metrics with the highest mean confidence (0.998), low standard deviation (0.035), and lowest entropy (0.004), suggesting consistent, reliable predictions. The uniform difficult cases count (91) across all models highlights a consistent set of challenging cases that could benefit from targeted improvement strategies. These metrics help quantify the \"black box\" nature of different architectures, with Transformer-based models surprisingly offering more consistent interpretations than conventional CNNs. Table 11 examines the later disease transition from MCI to AD, providing context for comparing early vs. late detection capabilities. ViT maintains strong performance across disease stages (0.9286 overlap, 0.0065 confusion), indicating consistent reliability throughout the disease continuum. EfficientNet shows a dramatic performance drop at this boundary (0.0000 overlap), revealing its specificity for early-stage detection only. ResNet demonstrates a significantly higher confusion rate (0.2270) at this boundary, explaining its tendency to misclassify MCI as AD (42.09%). These metrics highlight the varying abilities of architectures to maintain diagnostic consistency across disease progression. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Architecture** & \begin{tabular}{c} **MCI** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **Misclassified** \\ **as CN** \\ \end{tabular} & \begin{tabular}{c} **Misclassified** \\ **as AD** \\ \end{tabular} & \
\begin{tabular}{c} **Mean** \\ **Confidence** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **Uncertainty** \\ \end{tabular} \\ \hline ViT & 99.78\% & 0.11\% & 0.11\% & 0.998 & 0.004 \\ EfficientNet & 100.00\% & 0.00\% & 0.00\% & 0.399 & 1.533 \\ CNN & 98.46\% & 1.43\% & 0.11\% & 0.979 & 0.054 \\ DenseNet & 94.07\% & 4.84\% & 1.10\% & 0.890 & 0.350 \\ MobileNet & 93.19\% & 6.26\% & 0.55\% & 0.877 & 0.369 \\ VGG19 & 89.78\% & 5.16\% & 5.05\% & 0.806 & 0.561 \\ ResNet & 50.66\% & 7.25\% & 42.09\% & 0.522 & 0.713 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: MCI classification performance across architectures. 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
**EfficientNet** & 0.9500 & 0.5000 & 0.0000 \\ ViT & 0.9423 & 0.0017 & 0.0017 \\ CNN & 0.9270 & 0.0152 & 0.0032 \\ MobileNet & 0.8980 & 0.0562 & 0.0062 \\ DenseNet & 0.8956 & 0.0525 & 0.0122 \\ VGG19 & 0.8668 & 0.0784 & 0.0097 \\ ResNet & 0.8055 & 0.0692 & 0.0143 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Early biomarker detection at the cn to mci boundary. Here, ViT, VGG, ResNet, and CNN stand for Vision Transformer, Visual Geometry Group, Residual Network, Convolutional Neural Network, respectively. Table 12 reveals an inverse relationship between classification performance and interpretability. Traditional CNNs (CNN\({}_{\text{model}}\)) offer high interpretability with strong biomarker alignment and region specificity for hippocampal and entorhinal structures.