# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 1 Introduction  
Alzheimer's disease (AD) is a global health problem, being the most common cause of dementia and the fifth leading cause of death among people aged 65 and older [1]. An estimated 6.9 million Americans are living with Alzheimer's dementia, and this number is projected to reach a staggering 13.8 million by 2060 [1]. As a complex and progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and functional impairment, early and accurate detection of AD is crucial for effective intervention and management.  
Several traditional computational models, including linear regression [2] and nonlinear regression analyses, as well as linear discriminant analysis [3,4], have been constructed for analyzing complex datasets in critical domains such as healthcare. These techniques have produced results with acceptable accuracy, and evidence in the artificial intelligence (AI) literature indicates that the adoption of machine learning over the last decade has enhanced the capability to analyze and interpret complex and vast medical image datasets, identifying patterns for disease diagnosis, prediction, and treatment that elude traditional computational methods.  
In Alzheimer's disease, for instance, machine learning and deep learning models are increasingly being proposed for deployment in its detection and prediction [5]. However, the interpretability of these models remains a significant concern, as clinicians require clear and understandable explanations of the decision-making processes to trust and adopt AI-driven diagnostic tools in practice [6]. The successful adoption of predictive models in these settings heavily depends on how well decision-makers can understand, interpret, and justify the model's decisions, thereby building trust in their functionality.  
The need to utilize XAI to address these concerns has become more pronounced, especially considering the complex nature of Alzheimer's disease [7]. This is the motivation and driving force for this study. The principal contributions of this work are as follows:  
* This study integrates perturbation-based (LIME) and gradient-based (Saliency and Grad-CAM) interpretability approaches to visualize and explain an ensemble framework consisting of stacking and hard voting techniques, enhancing [PERSON]'s disease diagnosis through magnetic resonance imaging (MRI).
* A comprehensive ensemble architecture is developed that integrates multi-classifier Convolutional Neural Network architectures: VGG-19 for deep feature extraction, ResNet to address vanishing gradients, MobileNet and EfficientNet for efficient processing, DenseNet for feature reuse, a custom multi-classifier CNN, and a Vision Transformer for capturing complex spatial relationships.
* The proposed framework overcomes the limitations of single-model approaches, achieving high diagnostic accuracy and robustness while providing transparent decision-making processes.
* The implemented visualization techniques highlight critical MRI regions, enhancing clinicians' understanding of the model and making the decision-making process more transparent and trustworthy.
* Experimental results demonstrate that both stacking and hard voting provide a refined focus on diagnostically relevant areas, proving particularly sensitive to early-stage cognitive changes in MCI cases.  
This nuanced focus and superior interpretability position the proposed framework, which incorporates both stacking and hard voting ensemble methods, as a valuable approach in clinical settings where detailed, actionable insights are crucial. While the dataset is not publicly available, it can be accessed upon request to ADNI, subject to approval and adherence to their data use agreement. The integration of CNNs and ViTs within an explainable ensemble architecture offers a robust approach for AI-driven healthcare applications, setting the foundation for future advancements in precision diagnostics and model interpretability in medical imaging.