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Updated: May 20, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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3D Swin Transformer With Multi-Scale Dilated Convolution for White Blood Cell Hyperspectral Image Classification.

Yushi Yang1,2, Danfei Huang1,2, Yi Xie1,2

  • 1College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China.

Journal of Biophotonics
|May 19, 2026
PubMed
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This summary is machine-generated.

This study introduces a novel deep learning method for classifying white blood cells (WBCs) using hyperspectral imaging. The SwinMDC network significantly improves accuracy by utilizing spectral information for enhanced clinical diagnosis.

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Artificial Intelligence

Background:

  • Accurate white blood cell (WBC) classification is vital for clinical diagnosis.
  • Current methods often neglect spectral information, focusing mainly on spatial structures.
  • Hyperspectral imaging offers rich spectral data for enhanced analysis.

Purpose of the Study:

  • To develop an advanced deep learning model for WBC hyperspectral image classification.
  • To leverage spectral information for improved diagnostic accuracy.
  • To introduce the 3D Swin Transformer network (SwinMDC) for this task.

Main Methods:

  • Application of hyperspectral imaging technology for WBC analysis.
  • Proposal of the 3D Swin Transformer network (SwinMDC) incorporating multi-scale dilated convolutions.
Keywords:
Swin transformerhyperspectral imagemulti‐scale dilated convolutionwhite blood cell classification

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  • Utilizing a 3D multi-scale dilated convolution feature extractor for enhanced low-level representations.
  • Integration of a 3D window-based attention mechanism for capturing long-range dependencies.

    • Main Results:

    • The SwinMDC network achieved a 99.07% overall classification accuracy on a three-class WBC hyperspectral dataset.
    • Demonstrated superior performance compared to methods focusing solely on spatial structures.
    • Highlighted the effectiveness of incorporating spectral information through hyperspectral imaging.

    Conclusions:

    • The SwinMDC network shows significant potential for clinical WBC analysis.
    • Hyperspectral imaging combined with advanced deep learning offers a powerful approach for hematological diagnostics.
    • This method enhances the utilization of spectral information for more accurate cell classification.