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Related Experiment Video

Updated: Mar 11, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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MM FD ConvFormer multimodal frequency aware deformable CNN transformer network for robust brain tumor classification.

Anto Lourdu Xavier Raj Arockia Selvarathinam1, Umesh Kumar Lilhore2, Roobaea Alroobaea3

  • 1Department of Data Science and Analytics, College of Computing, Grand Valley State University, Michigan, USA.

Scientific Reports
|March 10, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces MM-FD-ConvFormer, a novel multimodal network for accurate brain tumor classification using MRI. The model enhances diagnosis by integrating spatial and frequency data, outperforming existing methods.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Accurate brain tumor classification from MRI is crucial for patient outcomes.
  • Current models often miss spectral features and tumor heterogeneity due to reliance on single-modal spatial data.
  • Limited generalization across datasets is a significant challenge for existing methods.

Purpose of the Study:

  • To develop a robust and interpretable multimodal network for brain tumor classification.
  • To improve the capture of spectral features and model tumor heterogeneity.
  • To enhance cross-dataset generalization capabilities.

Main Methods:

  • Proposed MM-FD-ConvFormer, a multimodal frequency-aware deformable CNN-Transformer network.
  • Integrated spatial MRI, frequency-domain MRI (Fourier/wavelet transforms), and multi-scale contextual features.
Keywords:
Brain tumor classificationCNN–Transformer hybridCross-dataset generalizationDeformable attentionFrequency-domain attentionMagnetic resonance imagingMultimodal learning

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  • Employed ConvNeXt V2 backbone, a parallel ConvNeXt branch, and Swin Transformer V2 with a deformable cross-modal attention mechanism.

    • Main Results:

    • MM-FD-ConvFormer consistently outperformed CNN baselines, transformers, and hybrid models in accuracy, macro-F1 score, and AUC.
    • Demonstrated robust performance across multiple public and external validation datasets.
    • Qualitative analyses confirmed interpretability and effective tumor localization.

    Conclusions:

    • MM-FD-ConvFormer provides a superior, interpretable, and generalizable solution for automated brain tumor classification.
    • The multimodal, frequency-aware approach effectively addresses limitations of single-modal methods.
    • The model shows significant potential for real-world clinical applications in neuro-oncology.