A Deep Fusion Network for Multiclass Classification of Alzheimer's Disease Using Multimodal Neuroimaging
DOI:
https://doi.org/10.22581/0385Keywords:
Alzheimer’s Disease, Deep Fusion Learning, Transformer Networks, Neuroimaging AI , Multiclass Classification, Medical Image AnalysisAbstract
Alzheimer’s disease (AD), the leading cause of dementia, lacks robust tools for early diagnosis, delaying therapeutic interventions. This study proposes DFT-AD, a deep-fusion framework for five-class Alzheimer’s disease classification using MRI images from the MAMI dataset. The five classes are Very Mild, Mild, Moderate, Severe, and None. A novel AI framework that integrates triple-stream deep learning (Inception-ResNet-v2, DenseNet-201) with cross-modal attention fusion to detect AD from structural MRI scans. Unlike existing methods, DFT-AD dynamically weights feature from each model via a learnable gating mechanism (α=0.78±0.03), validated by gradient-class activation maps (Grad-CAM). Trained on a rigorously augmented MAMI dataset contains 2,280 MRI images distributed across five classes. Our model achieves achieves 99.12% accuracy (95% CI: 98.2–99.8, n=227) in classifying AD across five stages. Statistical significance was confirmed via McNemar's test (p < 0.001 vs. all base models) outperforming standalone CNNs by +2.0% F1-score (0.99 vs. 0.97; 95% CI for difference: 1.2–2.8%). Crucially, DFT-AD identifies prodromal AD (MCI-to-AD conversion) with 93.7% sensitivity (95% CI: 91.2–96.2), surpassing CSF biomarkers (AUC: 0.99 vs. 0.91, *p* < 0.001, DeLong test). This work establishes a new paradigm for preclinical AD diagnosis, combining multimodal feature fusion with translational scalability.
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