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Vol. 6 No. 1 (2025)
2025 Edition

Articles

Multi-Modal Deep Learning Framework for Early Alzheimer's Disease Detection Using MRI Neuroimaging and Clinical Data Fusion

Published 2025-09-08

  • Wenyan Liu
    • ,
  • Shukai Fan
    • ,
  • Guifan Weng

Wenyan Liu
Electrical & Computer Engineering, Carnegie Mellon University, PA, USA

Shukai Fan
Data Sciences., University of Michigan, MI, USA

Guifan Weng
Computer Science, University of Southern California, CA, USA

Abstract

This study presents a comprehensive multi-modal deep learning framework that integrates three-dimensional MRI neuroimaging data with clinical assessments for enhanced early-stage Alzheimer's disease detection. The proposed methodology combines advanced convolutional neural networks for spatial brain structure analysis with attention-based mechanisms for clinical feature processing. A novel cross-domain fusion architecture enables effective integration of heterogeneous data modalities through learned feature representations. Experimental validation on a dataset of 2,847 participants demonstrates superior classification performance with 96.3% accuracy, 94.8% sensitivity, and 97.1% specificity compared to existing single-modality approaches. The framework incorporates interpretability mechanisms that highlight discriminative brain regions and clinical markers, providing clinically actionable insights for medical practitioners. Performance evaluation across diverse demographic groups confirms robust generalization capabilities essential for real-world deployment.

Keywords

Alzheimer's disease detection, multi-modal deep learning, MRI neuroimaging, clinical data fusion

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