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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Optimized spectroscopic regression for white blood cell quantification using metaheuristic feature selection.

Sonia Mustafa1, Gang Li1, Honghui Zeng2

  • 1Medical School of Tianjin University, Tianjin, 300072, China; State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China.

Computers in Biology and Medicine
|October 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized spectroscopic method for accurate White Blood Cell (WBC) count estimation. Biologically informed sub-models and metaheuristic optimization significantly improve WBC prediction accuracy for clinical decision-making.

Keywords:
M+N theory-based modelingMetaheuristic feature selectionMinimally invasive diagnosticsSpectral data analysisWhite blood cell prediction

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Area of Science:

  • Biomedical Engineering
  • Spectroscopy
  • Hematology

Background:

  • Accurate White Blood Cell (WBC) count is crucial for clinical decisions.
  • Existing spectroscopic methods face challenges with signal fidelity and confounding factors.
  • In-blood spectral acquisition offers enhanced signal integrity by reducing surface artifacts.

Purpose of the Study:

  • To develop an optimized spectroscopic framework for enhanced WBC count estimation.
  • To integrate metaheuristic feature selection with biologically informed sub-models.
  • To minimize spectral interference from hemoglobin and platelet variations for improved WBC prediction.

Main Methods:

  • Utilized high-dimensional optical spectrum data from 468 patients via fiber-optic probes.
  • Employed Genetic Algorithm (GA), Firefly Algorithm (FA), and Grey Wolf Optimization (GWO) for feature selection.
  • Developed biologically informed sub-models based on M+N theory, partitioning data by hemoglobin and platelet levels.
  • Trained Random Forest regression models within each sub-model.

Main Results:

  • The Firefly Algorithm with Random Forest achieved the highest R² (0.864) and lowest MAE (0.694) in the Low-HG/High-PLT sub-model.
  • GWO and GA also showed strong performance in specific sub-models (R²=0.809 and R²=0.889, respectively).
  • Sub-model-specific modeling significantly outperformed global regression (R²=0.73, RMSE=1.46), improving prediction precision.

Conclusions:

  • Biologically guided partitioning and metaheuristic optimization effectively enhance spectroscopic WBC diagnostics.
  • The proposed framework offers a practical and accurate alternative for point-of-care hematological analysis.
  • This method demonstrates potential for rapid and reliable clinical decision support.