Types of white blood cells

Pro Research Analysisby

Searched over 200M research papers

Analyzed relevant papers

Types of White Blood Cells: An Overview

White blood cells (WBCs), also known as leukocytes, are a crucial component of the immune system, responsible for defending the body against infections and foreign invaders. There are five primary types of white blood cells, each with distinct functions and characteristics. Understanding these types is essential for diagnosing and treating various health conditions.

Neutrophils

Function and Characteristics

Neutrophils are the most abundant type of white blood cells, making up about 50-70% of the total WBC count. They are the first responders to microbial infection and are primarily involved in phagocytosis, where they engulf and digest pathogens 126.

Clinical Significance

Neutrophil counts are critical in diagnosing infections, inflammation, and other conditions like leukemia. Abnormal levels can indicate acute bacterial infections or chronic inflammatory diseases 34.

Lymphocytes

Function and Characteristics

Lymphocytes account for about 20-40% of the total WBC count and are pivotal in the adaptive immune response. They are divided into three main types: T cells, B cells, and natural killer (NK) cells. T cells are involved in cell-mediated immunity, B cells produce antibodies, and NK cells provide rapid responses to virally infected cells 126.

Clinical Significance

Lymphocyte counts are essential for diagnosing viral infections, autoimmune diseases, and immune deficiencies. Elevated levels can indicate chronic infections or lymphocytic leukemia, while low levels may suggest immunodeficiency disorders 34.

Monocytes

Function and Characteristics

Monocytes make up about 2-8% of the total WBC count. They are the largest type of WBC and differentiate into macrophages and dendritic cells in tissues. Monocytes are involved in phagocytosis and play a role in the immune system's response to chronic infections 126.

Clinical Significance

Monocyte counts are used to diagnose chronic inflammatory conditions, infections, and hematological disorders. Elevated monocyte levels can indicate chronic infections or inflammatory diseases, while low levels may suggest bone marrow suppression 34.

Eosinophils

Function and Characteristics

Eosinophils constitute about 1-4% of the total WBC count. They are primarily involved in combating parasitic infections and play a role in allergic reactions and asthma. Eosinophils release toxic granules to destroy parasites and modulate inflammatory responses 126.

Clinical Significance

Eosinophil counts are crucial for diagnosing allergic conditions, parasitic infections, and certain autoimmune diseases. Elevated levels can indicate allergic reactions, parasitic infections, or eosinophilic disorders 34.

Basophils

Function and Characteristics

Basophils are the least common type of WBC, making up less than 1% of the total count. They are involved in inflammatory responses and play a role in allergic reactions by releasing histamine and other mediators 126.

Clinical Significance

Basophil counts are used to diagnose allergic reactions, chronic inflammation, and certain hematological conditions. Elevated levels can indicate allergic reactions or chronic myelogenous leukemia, while low levels are generally not clinically significant 34.

Conclusion

Understanding the different types of white blood cells and their functions is essential for diagnosing and treating various health conditions. Advances in deep learning and image processing have significantly improved the accuracy and efficiency of WBC classification, aiding in better clinical decision-making 12567. These technological advancements hold promise for developing more reliable and cost-effective diagnostic tools in the future.

Sources and full results

Most relevant research papers on this topic

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

The proposed method using generative adversarial networks and deep neural networks effectively classifies white blood cells into five types, with DenseNet-169 yielding a validation accuracy of 98.8%.

2020·

60citations

·Khaled Almezhghwi et al.

White blood cell type identification using multi-layer convolutional features with an extreme-learning machine

The MLANet-FS-ELM model accurately identifies white blood cell types (Lymphocytes, Monocytes, Neutrophils, Eosinophils, and Basophils) with 99.99% training accuracy and 99.12% testing accuracy, outperforming alternative methods in the literature.

2021·

26citations

·Altaf H. Khan et al.

Identification of Nine Novel Loci Associated with White Blood Cell Subtypes in a Japanese Population

Nine novel loci were identified in a Japanese population, suggesting unique and common functional roles of these genes in regulating white blood cell subtype counts and hematopoiesis.

Observational StudyHighly Cited

2011·

82citations

·Y. Okada et al.

Multiple Loci Are Associated with White Blood Cell Phenotypes

Multiple genomic loci are associated with white blood cell count and subtypes, revealing functional relationships across cell types and diverse ancestral backgrounds.

Meta-AnalysisHighly Cited

2011·

126citations

·M. Nalls et al.

Deep Learning Model for the Automatic Classification of White Blood Cells

The DenseNet121 deep learning model accurately classifies white blood cells in blood cell images with a sensitivity of 98.85% and specificity of 99.61%, making it a clinically useful tool for detecting WBC in blood cell images.

Highly Cited

2022·

125citations

·Sarang Sharma et al.

White blood cells detection and classification based on regional convolutional neural networks.

The study developed a computer-aided automated system that accurately detects and classifies white blood cell types in blood images, with ResNet50 showing the best performance with transfer learning.

Highly Cited

2019·

166citations

·Hüseyin Kutlu et al.

Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks

DenseNet161 outperforms other deep learning models in accurately classifying white blood cells in blood film images, improving clinical laboratory procedures and monitoring human health.

Rigorous Journal

2022·

13citations

·Thinam Tamang et al.

An Automated Multi-Level Convolutional Neural Network Approach for Classification of White Blood Cells

The proposed custom-built CNN framework accurately classifies white blood cell images with 98.70% accuracy, aiding medical professionals in diagnosing various abnormalities and disorders.

2023·

0citations

·M. Sangeetha et al.

Automatic segmentation, counting, size determination and classification of white blood cells

This paper presents an automatic system that accurately counts white blood cells, determines their sizes, and classifies them into five types, aiding in disease diagnosis.

Highly Cited

2014·

154citations

·S. Nazlibilek et al.

Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception

This study successfully classifies white blood cells using Inception and ResNet deep learning models, achieving high accuracy rates and reducing false positives in CBC tests.

Highly Cited

2018·

160citations

·Mehdi Habibzadeh et al.

Try another search

how does protein help you lose weight

space time and gravity

vitamin d deficiency dosage

inside of eyelids are white

pale skin from anemia

probiotic with lactobacillus