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Classification of Leukocytes

Classification of Leukocytes

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.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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Related Experiment Video

Updated: Aug 24, 2025

The Isolation and Characterization of Low- and Normal- Density Neutrophils from Whole Blood

The Isolation and Characterization of Low- and Normal- Density Neutrophils from Whole Blood

Published on: February 7, 2025

Classification of peripheral blood neutrophils using deep learning.

Tser-Rei Tseng¹, Hsuan-Ming Huang²

¹Clinical Laboratory, Taipei City Hospital Zhongxiao Branch, Taipei City, Taiwan.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|October 21, 2022

Summary

Deep learning models effectively classify developing neutrophils from diverse data sources, achieving 90.1% accuracy. Ensemble models show superior performance for immature and mature neutrophil identification in blood smears.

Keywords:

classification deep learning immature neutrophil peripheral blood smear

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Last Updated: Aug 24, 2025

The Isolation and Characterization of Low- and Normal- Density Neutrophils from Whole Blood

The Isolation and Characterization of Low- and Normal- Density Neutrophils from Whole Blood

Published on: February 7, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Area of Science:

Hematology
Computational Biology
Medical Imaging

Background:

Deep learning (DL) is established for white blood cell classification in peripheral blood smears.
Classification of developing neutrophils and DL performance across diverse datasets remain under-explored.

Purpose of the Study:

To investigate the classification performance of DL for immature and mature neutrophils.
To evaluate DL models using data from multiple imaging systems.

Main Methods:

Utilized three open-access datasets (CellaVision DM 96, DM 100, iCELL ME-150) comprising 26,050 images.
Trained 10 convolutional neural networks to classify six neutrophil precursor stages.
Employed an average ensemble model for enhanced classification performance.

Main Results:

The average ensemble model achieved a testing accuracy of 90.1%.
Sensitivity and specificity for the ensemble model exceeded 83.5% and 96.9%, respectively.
Ensemble models outperformed individual models in classifying neutrophil development stages.

Conclusions:

Deep learning demonstrates significant potential for classifying developing neutrophils.
The study highlights the robustness of DL across different imaging data sources.
Further research is recommended to enhance classification accuracy and clinical utility.