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

• URL: http://arxiv.org/abs/2507.16267v2
• Date: Wed, 23 Jul 2025 05:53:40 GMT
• Title: SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis
• Authors: Xinyue Yang, Meiliang Liu, Yunfang Xu, Xiaoxiao Yang, Zhengye Si, Zijin Li, Zhiwen Zhao,
• Abstract summary: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that predominantly affects the elderly population and currently has no cure.<n>SFNet is the first end-to-end deep learning framework that simultaneously leverages spatial and frequency domain information to enhance 3D MRI-based AD diagnosis.
• Score: 1.3401966602181168
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (ADNI) dataset demonstrate that SFNet outperforms existing baselines and reduces computational overhead in classifying cognitively normal (CN) and AD, achieving an accuracy of 95.1%.

Related papers

• 4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer's Diagnosis [24.771496672135395]
  We propose M2M-AlignNet: a geometry-aware co-attention network with latent alignment for early Alzheimer's diagnosis.<n>At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies.<n>We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondance between fMRI and sMRI as AD biomarkers.
  arXiv  Detail & Related papers  (2025-04-23T15:18:55Z)
• Early diagnosis of Alzheimer's disease from MRI images with deep learning model [0.7673339435080445]
  Alzheimer's disease is the most common cause of dementia worldwide. classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI) In this article, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images.
  arXiv  Detail & Related papers  (2024-09-27T15:07:26Z)
• Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
  In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models. We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
  arXiv  Detail & Related papers  (2024-01-11T19:07:58Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs [1.9450973046619378]
  We propose a novel model Longformer, a transformer network that performs attention mechanisms spatially on sMRIs at each time point. Our Longformer achieves state-of-the-art performance on two binary classification tasks of separating different stages of Alzheimer's disease (AD) using the AD dataset.
  arXiv  Detail & Related papers  (2023-02-02T06:38:00Z)
• Z-SSMNet: Zonal-aware Self-supervised Mesh Network for Prostate Cancer Detection and Diagnosis with Bi-parametric MRI [14.101371684361675]
  We propose a Zonal-aware Self-supervised Mesh Network (Z-SSMNet) Z-SSMNet adaptively integrates multi-dimensional (2D/2.5D/3D) convolutions to learn dense intra-slice information and sparse inter-slice information of the anisotropic bpMRI. A self-supervised learning (SSL) technique is proposed to pre-train our network using large-scale unlabeled data.
  arXiv  Detail & Related papers  (2022-12-12T10:08:46Z)
• Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers [72.047680167969]
  This article aims to introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods. We will detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies.
  arXiv  Detail & Related papers  (2022-04-01T22:48:08Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis [30.99183477161096]
  multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis. We propose a novel Hypergraph Generative Adversarial Networks(HGGAN) to generate multimodal connectivity of Brain Network from rs-fMRI combination with DTI.
  arXiv  Detail & Related papers  (2021-07-21T09:02:29Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• 4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification [69.62333053044712]
  We propose a 4D convolutional deep learning approach for ASD classification where we jointly learn from spatial and temporal data. We employ 4D neural networks and convolutional-recurrent models which outperform a previous approach with an F1-score of 0.71 compared to an F1-score of 0.65.
  arXiv  Detail & Related papers  (2020-04-21T17:19:06Z)
• A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study [0.0]
  We introduce an end-to-end algorithm capable of extracting features from full 4-D data using 3-D CNNs and 3-D Magnetical LSTMs. Our results show that the proposed model achieves state of the art results on single sites with F1-scores of 0.78 and 0.7 on NYU and UM sites, respectively.
  arXiv  Detail & Related papers  (2020-02-14T11:52:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.