A Lightweight Spatial-Spectral Dual-Branch Transformer Network for Classifying Polarized White Blood Cell Hyperspectral Images

YANG Yushi; YAN Jiaxuan; XIE Yi; QIU Lijia; HUANG Danfei

doi:10.11999/JEIT260124

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YANG Yushi, YAN Jiaxuan, XIE Yi, QIU Lijia, HUANG Danfei. A Lightweight Spatial-Spectral Dual-Branch Transformer Network for Classifying Polarized White Blood Cell Hyperspectral Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260124

Citation:

YANG Yushi, YAN Jiaxuan, XIE Yi, QIU Lijia, HUANG Danfei. A Lightweight Spatial-Spectral Dual-Branch Transformer Network for Classifying Polarized White Blood Cell Hyperspectral Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260124

Citation:

YANG Yushi, YAN Jiaxuan, XIE Yi, QIU Lijia, HUANG Danfei. A Lightweight Spatial-Spectral Dual-Branch Transformer Network for Classifying Polarized White Blood Cell Hyperspectral Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260124

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A Lightweight Spatial-Spectral Dual-Branch Transformer Network for Classifying Polarized White Blood Cell Hyperspectral Images

doi: 10.11999/JEIT260124 cstr: 32379.14.JEIT260124

YANG Yushi^{1, 2},
YAN Jiaxuan^{1, 2},
XIE Yi^{1, 2},
QIU Lijia³,
HUANG Danfei^{1, 2
,
,}

1.
College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, 130022, China
2.
Zhongshan Institute, Changchun University of Science and Technology, Zhongshan, 528400, China
3.
Chaonan Hospital of Zhujiang Hospital, Southern Medical University, Shantou, 515144, China

Funds: National Natural Science Foundation of China (62105245)

Received Date: 2026-01-30
Accepted Date: 2026-06-29
Rev Recd Date: 2026-06-29

Available Online: 2026-07-08

Abstract

Abstract

Objective White Blood Cell (WBC) classification and morphology are essential in routine blood analysis and provide important information for disease diagnosis and health assessment. Current WBC classification mainly relies on hematology analyzers and manual microscopic examination. Hematology analyzers cannot acquire cellular images, limiting classification accuracy when abnormal cellular characteristics are present, whereas manual microscopic examination depends on operator experience and is susceptible to human error. Although automated WBC classification based on deep learning and computer vision has attracted considerable attention, methods using stained images are sensitive to staining conditions and image quality. In addition, conventional hyperspectral imaging has limited ability to distinguish WBC subtypes with highly similar morphological and spectral characteristics. To address these limitations, this study combines Polarized Hyperspectral Imaging (PHSI) with deep learning and proposes a lightweight classification framework for polarized hyperspectral WBC images, providing an efficient and reliable approach for clinical decision support. Methods A polarized hyperspectral microscopic imaging system is established to acquire images at multiple polarization angles. Based on Stokes Vector Theory, polarization parameters are calculated to generate multidimensional data cubes containing both light intensity and polarization-state information. Degree of Linear Polarization (DOLP) images are then computed to construct a PHSI dataset of WBCs. To exploit the multidimensional characteristics of PHSI, a Lightweight Spatial-Spectral Dual-Branch Transformer Network (LSDBT) is proposed. After preprocessing, the input data are fed into a dual-branch feature extraction module that separately extracts local spatial features and joint spatial-spectral features. An adaptive scaling factor is introduced to fuse the two feature streams and balance their contributions, enabling effective utilization of the multidimensional information contained in PHSI. A lightweight Swin Transformer backbone performs effective global feature modeling while reducing computational complexity. Global average pooling and a fully connected layer are used for classification. Model performance is evaluated using Overall Accuracy (OA), Precision, Recall, Specificity, and F1-Score. Ablation studies, comparative experiments, and feature visualization are conducted to validate the proposed method. Results and Discussions The DOLP spectra and PHSI visualizations of monocytes, lymphocytes, and neutrophils (Figures. 5 and 6) demonstrate different polarization characteristics that reflect the selective absorption and scattering of light by their internal structures. Compared with conventional intensity images, PHSI improves image contrast and provides additional polarization information that enhances discrimination among WBC types. The proposed LSDBT achieves an OA of 99.29% on the test set, with consistently high classification performance across all cell categories (Table 1). Analysis of the adaptive scaling factor shows that classification performance first improves and then decreases slightly as the scaling factor increases, with the optimal value of 3 providing the best balance between spatial and spatial-spectral features (Figure. 8). Ablation experiments (Tables 2 and 3) demonstrate that the dual-branch feature extraction module substantially improves classification performance, whereas the lightweight design greatly reduces computational complexity and model parameters with only a marginal reduction in accuracy. Compared with conventional hyperspectral imaging, the PHSI dataset achieves higher classification accuracy with all evaluated classifiers, indicating that polarization information provides complementary physical features that improve discrimination among WBC types (Table 4). Comparisons with representative methods show that LSDBT achieves the best overall classification performance across multiple evaluation metrics (Table 5). Furthermore, t-SNE visualization (Figure. 9) shows compact intra-class distributions and clear separation among different cell types, confirming the strong discriminative capability of the learned features. Although LSDBT does not have the lowest computational cost among the compared methods, it achieves the best balance between classification performance, model size, and computational efficiency (Table 6). Conclusions This study proposes a lightweight dual-branch Transformer network for polarized hyperspectral WBC classification. To the best of our knowledge, this is the first study to combine PHSI with deep learning for WBC classification. Comparative experiments with conventional hyperspectral imaging validate the superiority of PHSI for WBC classification. The proposed LSDBT integrates spatial and spatial-spectral information through a dual-branch feature extraction module and performs efficient global feature modeling using a lightweight Swin Transformer backbone. The network maintains high classification performance while substantially reducing computational complexity and model parameters. These results demonstrate that LSDBT provides an accurate and computationally efficient solution for automated WBC classification and supports the application of PHSI in cellular microscopic analysis and clinical auxiliary diagnosis.
- Polarized Hyperspectral Imaging,
- White Blood Cell Classification,
- Swin Transformer

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References(23)

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