A Dense Feature Fusion Framework for Multi-Modal Neuroimaging in Alzheimer’s and Parkinson’s Disease

Abstract

Neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) present significant obstacles in early diagnosis due to the complex interplay of structural and functional biomarkers. Multi-modal neuroimaging provides complementary information, yet integrating heterogeneous features remains a persistent challenge. In this work, we propose Dense Feature Fusion Network (DFF-Net), an end-to-end deep learning framework that leverages MRI and PET modalities through a dense feature fusion block and cross-modal attention mechanism. Our approach facilitates richer representation learning by preserving both modality-specific and shared features. We evaluate DFF-Net on benchmark datasets such as ADNI and PPMI, achieving superior performance over baseline fusion strategies in terms of accuracy, AUC, and F1-score. Furthermore, an interpretability analysis through attention maps pinpoints critical brain regions involved in neurodegenerative progression. The proposed model demonstrates the strong potential of dense feature fusion in improving clinical decision-making for early and accurate detection of neurodegenerative disorders.