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Related Concept Videos

Classification of Leukocytes01:30

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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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An adult in good health typically has between 4,500 and 11,000 leukocytes, or white blood cells, per microliter of blood, which constitutes about 1% of the total blood volume. Unlike red blood cells, white blood cells contain a nucleus and other cellular organelles but do not have hemoglobin. Most white blood cells reside in connective tissues, particularly in lymphatic organs such as the lymph nodes, with only a small fraction present in circulating blood.
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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
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Leukocyte disorders can lead to either leukopenia, characterized by an abnormally low leukocyte count, or leukocytosis, marked by a very high leukocyte number.
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Common myeloid progenitors (CMPs) are oligopotent cells that can differentiate into granulocytes and macrophages. Granulocytes and macrophages are essential for protecting the body against bacterial, viral, or fungal infections. They migrate from the bone marrow into the circulating blood to reach specific tissue sites where they differentiate and help in immune surveillance. However, they survive only for a few days and must be continuously made available to the organism to maintain a robust...
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Leukocyte subtype classification with multi-model fusion.

Yingying Ding1, Xuehui Tang2, Yuan Zhuang2

  • 1School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin, 541004, China.

Medical & Biological Engineering & Computing
|April 3, 2023
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Summary

This study introduces an automated leukocyte classification system for diagnosing leukemia. The novel two-stage approach accurately identifies 11 white blood cell types, improving diagnostic efficiency.

Keywords:
Deep learningFine-grained classificationLeukocyteMedical image

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Area of Science:

  • Hematology
  • Medical Imaging
  • Machine Learning

Background:

  • Accurate leukocyte classification is vital for diagnosing hematologic malignancies like leukemia.
  • Traditional methods are labor-intensive and prone to subjective errors.
  • Automated systems are needed to improve diagnostic speed and accuracy.

Purpose of the Study:

  • To develop a robust leukocyte classification system for identifying 11 distinct white blood cell types.
  • To assist radiologists in the diagnosis of leukemia through accurate cell classification.
  • To provide technical support for advancing the capabilities of hematology analyzers.

Main Methods:

  • A two-stage classification strategy was employed, combining multi-model fusion with ResNet for initial classification based on shape features.
  • A support vector machine was utilized for fine-grained classification of lymphocytes, focusing on texture features.
  • The system was trained and validated on a dataset of 11,102 microscopic leukocyte images across 11 classes.

Main Results:

  • The proposed method achieved high accuracy in classifying leukocyte subtypes.
  • Key performance metrics included accuracy (97.03%), sensitivity (96.76%), specificity (99.65%), and precision (96.54%).
  • The multi-model fusion approach demonstrated significant effectiveness in classifying 11 leukocyte classes.

Conclusions:

  • The developed leukocyte classification model offers a reliable and automated solution for identifying white blood cell types.
  • This system has the potential to significantly enhance the performance and efficiency of hematology analyzers.
  • Accurate classification supports improved diagnostic capabilities for hematologic malignancies.