# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 5 Conclusions and Future Work  
This research has proposed a comprehensive ensemble approach for AD diagnosis using the ADNI dataset. The proposed pipeline leveraged a diverse set of models including CNN architectures and a ViT to enhance accuracy and robustness of the analysis for the three-way classification problem (CN, MCI, AD). Stacking emerged as the most effective ensemble technique, achieving a diagnostic accuracy of 98.0%, while hard voting closely followed at 97.0%. The stacking method demonstrated a refined focus on critical neurodegenerative regions such as the hippocampus and cortical areas, which are vital for detecting early cognitive impairments like MCI. The use of multiple visualization methods highlighted the spatial attention patterns and provided clinicians with actionable insights, making the diagnostic process both transparent and interpretable. This work advances the field of XAI in medical imaging by demonstrating that accuracy and interpretability can coexist without compromise. The multi-method explainability approach using Grad-CAM, LIME, and Saliency Maps revealed critical insights into model decision-making processes, with each method providing complementary perspectives on the classification decisions. This comprehensive explainability framework addresses the limitations of individual methods, providing clinically relevant insights that enhance the trustworthiness of the classification results. Future research should prioritize integrating multimodal data, such as PET scans, genetic markers, and cognitive test scores, which would enrich the diagnostic precision and offer a more comprehensive understanding of disease progression. Future work may implement domain-specific pretraining on large medical imaging datasets before fine-tuning on Alzheimer's datasets. Additionally, incorporating anatomical atlases to precisely map visualization outputs to specific brain structures and conducting correlation analysis between model attention patterns and quantitative measurements of hippocampal and entorhinal cortex volumes would further enhance the approach. Given the subtle changes associated with early-stage cognitive impairments, additional efforts should focus on improving sensitivity to these features, possibly by incorporating temporal modeling to analyze longitudinal MRI data and capture disease progression over time. The error analysis highlighted that the most challenging misclassification type was identifying severe cases (AD) incorrectly as normal (CN), suggesting that focused refinement of models for these critical error cases could further improve clinical utility. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Ref** & **Classifier** & **Best Accuracy Score** & **XAI Method** & **Dataset** \\ \hline [PERSON] et al. [32] & DenseNet169 and DenseNet201 & 96.00\% & Saliency Maps, Grad-CAM & MRI Scans OASIS \\ [PERSON] et al. [33] & CNN & 94.96\% & LIME & ADNI MRI \\ [PERSON] et al. [34] & Random Forest & 93.95\% & SHAP & ADNI Multimodal \\  & CNN + MKSCDDL + & & Scandent Decision & with 12 Features \\ [PERSON] et al. [31] & Scandent Decision & 98.27\% & Trees, Discriminative & ADNI MRI \\  & Trees & & Dictionary Learning & \\ \hline
**This Study** & Stacking Ensemble & 98.00\% &
\begin{tabular}{c} Grad-CAM, LIME, \\ Saliency Map \\ \end{tabular} & ADNI MRI \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performance Comparison of Ensemble Techniques in Alzheimer’s Classification and Related Methods. Finally, validation in larger, more diverse clinical datasets will be essential to ensure the generalizability of these findings across different patient populations and imaging protocols. Conceptualization was performed by O.T.A., B.O., T.E.A., O.O.E.P., A.O.A., M.M.R. and F.K. ; methodology was developed by O.T.A. ; software implementation was carried out by O.T.A. ; validation was conducted by B.O., F.K. and M.M.R. ; formal analysis and investigation were undertaken by O.T.A. ; resources were provided by O.T.A. and M.M.R. ; data curation was handled by O.T.A.