CDFNet based fusion network for interpretable Alzheimer's Disease prediction using hybrid imaging and clinical meta-ensemble learning

Abstract

This work presents a CNN-DenseNet Fusion Network (CDFNet) for interpretable Alzheimer's disease (AD) stage classification using two complementary data streams: structured clinical attributes and MRI scans. A stacking ensemble on the clinical dataset identified Functional Assessment, ADL, and MMSE as dominant predictors, achieving precision 0.98 and recall 0.97, confirming clinically meaningful feature usage. The MRI pipeline, powered by CDFNet, reached 97.4% accuracy and macro F1 of 0.98, outperforming single-branch CNN and DenseNet121 baselines. t-SNE clustering, ROC curves (AUC ? 0.998), and Grad-CAM maps over hippocampal and ventricular regions validated both discrimination and biological plausibility. Together, these pipelines form a stable, leakage-safe, and clinically aligned framework for multimodal dementia staging.