Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

S3F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network.

Md Saiful Bari Siddiqui, Mohammed Imamul Hassan Bhuiyan

    IEEE Journal of Biomedical and Health Informatics
    |April 9, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.

    Journal of digital imaging·2023
    Same author

    A Lightweight CNN Model for Detecting Respiratory Diseases From Lung Auscultation Sounds Using EMD-CWT-Based Hybrid Scalogram.

    IEEE journal of biomedical and health informatics·2020
    Same author

    Sleep stage classification using single-channel EOG.

    Computers in biology and medicine·2018
    Same author

    Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting.

    Computer methods and programs in biomedicine·2017
    Same author

    A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features.

    Journal of neuroscience methods·2016
    Same journal

    An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    PGCASurv: A Prior-Guided Cross-Attention Framework for Dynamic Survival Model with Longitudinal Data.

    IEEE journal of biomedical and health informatics·2026
    See all related articles

    This study introduces the Spatial-Spectral Summarizer Fusion Network (S³F-Net) for medical image analysis, enhancing Convolutional Neural Networks (CNNs) by integrating spatial and spectral data. The novel S³F-Net significantly improves diagnostic accuracy across diverse medical imaging datasets.

    Area of Science:

    • Medical Image Analysis
    • Deep Learning
    • Computer Vision

    Background:

    • Convolutional Neural Networks (CNNs) excel at spatial feature extraction in medical imaging.
    • Existing CNNs often neglect global patterns and frequency-domain information, limiting performance.
    • A dual-domain approach is needed to capture both spatial and spectral characteristics for comprehensive analysis.

    Purpose of the Study:

    • To propose the Spatial-Spectral Summarizer Fusion Network (S³F-Net), a novel dual-branch framework for medical image analysis.
    • To integrate spatial and spectral representations for improved feature learning.
    • To evaluate the generalizability and efficacy of S³F-Net across diverse medical imaging modalities.

    Main Methods:

    • Developed S³F-Net, fusing a deep spatial CNN with a shallow spectral encoder (SpectraNet).

    Related Experiment Videos

  • Introduced SpectraNet's SpectralFilter layer, applying learnable filters to the Fourier spectrum for global feature extraction.
  • Evaluated S³F-Net on HAM10000, BUSI, BRISC2025, and Chest X-Ray Pneumonia datasets using different fusion strategies.

    • Main Results:

    • S³F-Net consistently outperformed spatial-only baselines, with accuracy improvements up to 5.13%.
    • Achieved state-of-the-art 98.76% accuracy on BRISC2025 using Bilinear Fusion.
    • Demonstrated superior performance on the Chest X-Ray Pneumonia dataset (93.11% accuracy) with Concatenation Fusion.

    Conclusions:

    • The dual-domain approach of S³F-Net is a powerful and generalizable paradigm for medical image analysis.
    • S³F-Net dynamically adjusts reliance on spatial and spectral branches based on input pathology.
    • The framework offers significant improvements over traditional methods, advancing medical image diagnostics.