# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion
### Performance Analysis Using the Confusion Matrix
#### 4.1.1 Analysis of misclassifications and hard voting performance
DenseNet, ResNet, and VGG19 tied for the highest overall accuracy (44.44%) on misclassified samples, but with different strengths. ResNet excelled at correctly classifying critical 2\(\rightarrow\)0 errors (75% accuracy), while DenseNet performed well on other error types but failed on 2\(\rightarrow\)0 errors. The VIT model failed to correctly classify any misclassified samples, suggesting limited value in the ensemble for difficult cases. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Error Type** & **\% of Errors** & **Error Category** & **Clinical Impact** & **Best Models** & **Best Accuracy** & **VIT Accuracy** \\ \hline
2\(\rightarrow\)0 & 44.44\% & False Negative & High & ResNet & 75.00\% & 0.00\% \\
0\(\rightarrow\)1 & 22.22\% & False Positive & Medium & DenseNet, VGG19 & 100.00\% & 0.00\% \\
1\(\rightarrow\)2 & 11.11\% & False Positive & Medium–High & Multiple & 100.00\% & 0.00\% \\
2\(\rightarrow\)1 & 11.11\% & False Negative & High & DenseNet & 100.00\% & 0.00\% \\
1\(\rightarrow\)0 & 11.11\% & False Negative & Medium–High & Multiple & 100.00\% & 0.00\% \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of misclassification patterns for stacking calssifier. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Model** & **2\(\rightarrow\)0 (4)** & **0\(\rightarrow\)1 (2)** & **1\(\rightarrow\)2 (1)** & **2\(\rightarrow\)1 (1)** & **1\(\rightarrow\)0 (1)** & **Overall** \\ \hline DenseNet & 0.00\% & 100.00\% & 100.00\% & 100.00\% & 0.00\% & 44.44\% \\ ResNet & 75.00\% & 50.00\% & 0.00\% & 0.00\% & 0.00\% & 44.44\% \\ VGG19 & 25.00\% & 100.00\% & 0.00\% & 0.00\% & 100.00\% & 44.44\% \\ CNN & 25.00\% & 50.00\% & 0.00\% & 0.00\% & 100.00\% & 33.33\% \\ EfficientNet & 0.00\% & 0.00\% & 100.00\% & 0.00\% & 100.00\% & 22.22\% \\ MobileNet & 0.00\% & 50.00\% & 100.00\% & 0.00\% & 0.00\% & 22.22\% \\ ViT & 0.00\% & 0.00\% & 0.00\% & 0.00\% & 0.00\% & 0.00\% \\ \hline \end{tabular}
\end{table} 
Table 5: Model performance on error types. Here, ViT, VGG, ResNet, and CNN stand for Vision Transformer, Visual Geometry Group, Residual Network, Convolutional Neural Network, respectively. Figure 7: Hard voting model accuracy comparing individual models. Table 6 examines each misclassified sample individually, showing which models correctly classified each instance and the level of model agreement. The highest agreement (57.14%) was for instance 1550. For critical 2\(\rightarrow\)0 errors, model agreement was notably low (averaging 17.86%), highlighting the difficulty in correctly identifying these cases. No instance was correctly classified by all models, indicating that even the best ensemble cannot fully resolve certain challenging examples. Table 7 focuses specifically on the most concerning 2\(\rightarrow\)0 error type where severe abnormalities are misclassified as normal. ResNet's superior performance is highlighted, correctly identifying three out of four instances (75% accuracy), while most other models failed completely. This analysis suggests that greater weight should be given to ResNet's predictions for potential class 2 cases in future ensemble configurations. Figure 8 depicts accuracy comparison across seven deep learning models in our stacking ensemble. ViT and CNN demonstrate the highest accuracy (99.3% and 97.8%), while EfficientNet shows the lowest (41.4%). Despite this variation, our ensemble achieves a 99.59% overall accuracy, exceeding any individual model. The performance diversity is beneficial--lower-performing models contribute unique classification patterns that resolve specific error cases where higher-performing models falter. This confirms our ensemble effectively balances misclassification patterns across architectures, resulting in a more robust classification system.