# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion
### Performance Evaluation Using ROC and AUC Metrics  
The performance of the ensemble classifiers was evaluated using ROC curves and AUC metrics for the classes CN, MCI, and AD. Figure (a)a presents the ROC curves for the hard voting ensemble classifier, illustrating its performance in distinguishing between the three classes: CN, MCI, and AD. The AUC values are exceptionally high, with CN and MCI both achieving an AUC of 0.98, while AD achieves an AUC of 0.97. These results highlight the strong predictive accuracy of the hard voting ensemble, particularly for CN and MCI. Although the AUC for AD is marginally lower, it still demonstrates excellent classification performance. Figure 10 presents the performance metrics of the individual base models that comprise the ensemble classifiers. The base models demonstrate varied performance across accuracy and ROC-AUC metrics. ViT emerges as the top performer with 0.995 accuracy and a perfect 1.000 ROC-AUC score, followed closely by CNN with 0.978 accuracy and 0.999 ROC-AUC. DenseNet and MobileNet3 show comparable performance with accuracy values of approximately 0.92 and ROC-AUC scores around 0.99. VGG19 exhibits a notable gap between its moderate accuracy (0.860) and strong ROC-AUC (0.969). ResNet achieves moderate results with 0.649 accuracy but a relatively high 0.887 ROC-AUC. EfficientNet  
Figure 8: Stacking model accuracy comparing individual models. Figure 9: ROC curves for (**a**) hard voting and (**b**) stacking ensemble classifiers in Alzheimer’s disease classification. significantly underperforms compared to all other models, with only 0.414 accuracy and 0.522 ROC-AUC. When comparing individual model performance to the ensemble results, it is evident that while the hard voting ensemble does not surpass the top individual performers, it successfully mitigates the impact of weaker models, delivering consistently high performance across all classes. Moreover, the AUC results for CN, MCI, and AD do not strongly indicate overfitting for several reasons:  
* **Reasonable progression of difficulty**: the slight performance differences between classes (CN being easiest and AD being hardest to classify in hard voting) align with clinical expectations, as cognitive normal cases are typically more distinct than disease states. * **Consistent performance across ensembles**: if severe overfitting were present, we might see unrealistically perfect performance across all classes or unexpected patterns (such as AD being easier to classify than CN). * **Ensemble techniques inherently reduce overfitting**: the very purpose of ensemble methods is to improve generalization by combining diverse models, which helps mitigate any overfitting present in the individual models. * **Performance ceiling effect**: the near-perfect AUC values in the stacking ensemble could represent a genuine ceiling effect rather than overfitting, especially if the classification task is relatively straightforward with well-separated classes. Comparative Performance Analysis of CNN and Transformer Architectures for Alzheimer's Disease Progression Detection  
Table 8 presents the core classification metrics for MCI detection across all seven architectures. While ViT and EfficientNet demonstrate exceptional accuracy (99.78% and 100%, respectively), their confidence and uncertainty metrics reveal important differences. ViT combines high accuracy with high confidence (0.998) and low uncertainty (0.004), whereas EfficientNet, despite achieving perfect accuracy, exhibits low confidence (0.399) and high uncertainty (1.533), suggesting potential overfitting. CNN\({}_{\text{model}}\) offers a balanced performance profile with 98.46% accuracy, high confidence (0.979), and low uncertainty (0.054), making it particularly robust for clinical applications. Figure 10: Combined accuracy and AUC-ROC metrics for individual model in Alzheimer’s disease classification. Table 9 focuses specifically on early detection capabilities at the critical CN-to-MCI transition.