Multimodal Deep Learning (DL) for Early Alzheimer's Disease (AD) Detection: Leveraging MRI and Clinical Data

Alzheimer’s Disease is a degenerative brain disorder that progressively impairs cognitive functions, particularly memory, and poses a significant burden on individuals and healthcare systems worldwide. Timely and accurate diagnosis of AD in its early stages is crucial to enable effective interventions and support patient care. Traditional diagnostic approaches often rely on either structural brain imaging or clinical evaluation, yet using one modality in isolation limits the ability to capture the complexity of the disease. This study introduces a multimodal deep learning framework designed to integrate structural Magnetic Resonance Imaging (MRI) with comprehensive clinical data for early detection of AD. The proposed system employs a three-dimensional convolutional neural network (3D-CNN) to analyze volumetric MRI scans and a multi-layer perceptron (MLP) to process structured clinical features. To enhance representational learning, the model applies an attention-based fusion strategy, including Transformer mechanisms, which enable it to focus on the most relevant modality-specific features. Furthermore, an ensemble learning approach combines the predictions of the individual modalities and the multimodal fusion branch, significantly improving the overall diagnostic performance. While the framework's proof-of-concept validation was conducted on a simulated dataset, the results demonstrate a high degree of accuracy and offer a strong basis for its application to real-world clinical data. We demonstrate that our ensemble model achieves a superior accuracy of 97.0% and an AUC of 0.985, outperforming unimodal and non-attentive multimodal baselines. The framework's explain ability, highlighted by Grad-CAM and SHAP, offers valuable insights into the model's decision-making process, a critical step towards its clinical acceptance.