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Classification of Leukocytes01:30

Classification of Leukocytes

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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.
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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Structure and Function of Leukocytes01:21

Structure and Function of Leukocytes

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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.
White blood cells protect the body...
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Classification of Connective Tissues01:30

Classification of Connective Tissues

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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.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Updated: Aug 16, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural

Thinam Tamang1, Sushish Baral2, May Phu Paing3

  • 1Madan Bhandari Memorial College, New Baneshwor, Kathmandu 44600, Nepal.

Diagnostics (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

Deep learning models accurately classify white blood cell types from images. DenseNet161 achieved superior performance in this automated blood cell analysis, improving diagnostic efficiency.

Keywords:
complete blood clountdeep learninglabel smoothingmixup augmentationnormalization

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Area of Science:

  • Hematology
  • Medical Imaging
  • Computational Biology

Background:

  • White blood cells (WBCs) are crucial for immune defense, with specific types (neutrophils, eosinophils, basophils, monocytes, lymphocytes) performing distinct functions.
  • Accurate quantification of WBCs via complete blood count (CBC) tests is vital for health monitoring.
  • Traditional methods can be time-consuming; deep learning offers potential for faster, accurate classification of blood cells from images.

Purpose of the Study:

  • To evaluate the performance of various deep learning models, particularly Convolutional Neural Network (CNN) architectures, for classifying white blood cell types from blood film images.
  • To compare model efficacy based on key performance metrics including accuracy, F1-score, recall, and precision.
  • To investigate the impact of advanced optimization techniques on model performance.

Main Methods:

  • Exploitation of state-of-the-art deep learning models and CNN variations.
  • Comparative analysis of model performance using metrics like accuracy, F1-score, recall, and precision.
  • Application of optimization techniques including normalization, mixed-up augmentation, and label smoothing to enhance a selected model.

Main Results:

  • DenseNet161 demonstrated superior performance compared to other evaluated deep learning models.
  • The study provides a quantitative comparison of different CNN architectures for WBC classification.
  • Optimization techniques further improved the performance of the DenseNet161 model.

Conclusions:

  • Deep learning, specifically CNNs like DenseNet161, offers a highly accurate and efficient method for classifying white blood cells from images.
  • Advanced optimization strategies can further enhance the diagnostic capabilities of these automated systems.
  • This approach has the potential to significantly improve the speed and accuracy of routine hematological analyses.