SFNet: A Spatio-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis

2025-07-22

AI Summary

Existing Alzheimer’s disease (AD) diagnostic models predominantly rely on single-domain (spatial or frequency) feature extraction from 2D MRI, limiting their capacity to comprehensively characterize complex 3D neuropathological patterns. To address this, we propose SFNet—a novel end-to-end spatial-frequency dual-domain fusion deep learning framework specifically designed for 3D MRI. Our key contributions are: (1) the first joint modeling of local spatial structures and global frequency-domain representations in 3D MRI; (2) an enhanced dense convolutional network integrated with a global frequency module to synergistically extract complementary dual-domain features; and (3) a multi-scale attention mechanism enabling adaptive feature weighting and refinement. Evaluated on the ADNI dataset, SFNet achieves 95.1% classification accuracy—significantly outperforming state-of-the-art baselines—while reducing computational overhead. This work establishes a new paradigm for early, precise AD identification via holistic 3D neuroimaging analysis.

Technology Category

Computer Vision: 3D Computer VisionMachine Learning: Transfer, Domain Adaptation, Multi-Task LearningKnowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphsSearch and Retrieval-Augmented AI: Web query analysis, representation and understandingWeb Mining and Content Analysis: Large pretrained models with web data

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that predominantly affects the elderly population and currently has no cure. Magnetic Resonance Imaging (MRI), as a non-invasive imaging technique, is essential for the early diagnosis of AD. MRI inherently contains both spatial and frequency information, as raw signals are acquired in the frequency domain and reconstructed into spatial images via the Fourier transform. However, most existing AD diagnostic models extract features from a single domain, limiting their capacity to fully capture the complex neuroimaging characteristics of the disease. While some studies have combined spatial and frequency information, they are mostly confined to 2D MRI, leaving the potential of dual-domain analysis in 3D MRI unexplored. To overcome this limitation, we propose Spatio-Frequency Network (SFNet), the first end-to-end deep learning framework that simultaneously leverages spatial and frequency domain information to enhance 3D MRI-based AD diagnosis. SFNet integrates an enhanced dense convolutional network to extract local spatial features and a global frequency module to capture global frequency-domain representations. Additionally, a novel multi-scale attention module is proposed to further refine spatial feature extraction. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ANDI) dataset demonstrate that SFNet outperforms existing baselines and reduces computational overhead in classifying cognitively normal (CN) and AD, achieving an accuracy of 95.1%.

Problem

• Diagnosing Alzheimer's disease using 3D MRI data
• Combining spatial and frequency domain information effectively
• Improving accuracy and reducing computational overhead in AD diagnosis

Innovation

• SFNet combines spatial and frequency domain information
• Uses dense convolutional network for spatial features
• Introduces multi-scale attention for refined feature extraction