# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 3 Methodology
Other sequences, such as T2-FLAIR and T2-TSE with Fat Saturation, enhance tissue contrast for visualizing structural abnormalities, while MPRAGE delivers high-resolution images of cortical and subcortical regions. Standardized imaging protocols, including field mapping for correcting distortions and B1-calibration scans, ensure data consistency across participants and visits. Demographic details, such as sex (male or female), age (ranging from early 60 s to over 90 years), and visit timepoints, further enhance the dataset's utility. This diversity ensures the dataset's applicability to real-world clinical scenarios and supports generalized research across diverse populations. Sample MRI images, shown in Figure 1, provide a visual representation of the dataset, illustrating distinct patterns and features for each diagnostic group: CN, MCI, and AD. These visualizations highlight structural differences in the brain, which are critical for training ML models. _The dataset preprocessing_ was specifically designed to support custom multi-classifier-CNN, ViT, and transfer learning models. In this study, all available MRI sequences from the ADNI dataset (3-plane_localizer, Axial_PD_T2_FSE, Axial_T2-FLAIR, Axial_T2-Star, B1-Calibration_Body, B1-Calibration_PA, Field_Mapping, MP-RAGE_REPEAT, MP-RAGE, MPRAGE_SENSE2, MPRAGE, Double_TSE, MPRAGE_SENS, SURVEY) were processed as input to the models. Instead of selecting specific sequences, each DICOM image was treated as an independent input sample, regardless of which sequence it belongs to. The grayscale DICOM images were transformed into RGB format, standardized to a pixel value range of 0-255, and resized to a fixed resolution of 224 \(\times\) 224 pixels using area-based interpolation. Subsequently, pixel values were normalized to the range [0, 1] by dividing by 255.0, a crucial step to ensure efficient training and enhanced convergence of deep learning models. Figure 1: Sample MRI images labeled to show structural brain differences. ### Model Development

Transitioning from the data preprocessing stage, the next phase centers on the development of deep learning models, incorporating a _custom-built_ multiclassifier CNN, _transfer learning models_, and the _Vision Transformer_. This stage focuses on leveraging advanced architectures to extract meaningful features and improve classification accuracy, forming the core of the Alzheimer's disease detection framework. _First, a custom-built CNN_ for multiclass classification tasks such as AD detection and classification was designed. The model structure is detailed in Algorithm 2. _Secondly, the transfer learning_ architecture utilizes pre-trained models initialized with ImageNet weights to leverage their robust feature extraction capabilities for the target classification in Algorithm 3. _Vision Transformer_ was also included in the ensemble module to enhance the overall performance. In particular, the ViT-B16 architecture introduces a transformative approach to image classification by leveraging the self-attention mechanism of Transformers, contrasting traditional CNNs as described in Algorithm 4.