Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

3.5K
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...
3.5K
Structure and Function of Leukocytes01:21

Structure and Function of Leukocytes

2.5K
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...
2.5K
Flow Cytometry01:23

Flow Cytometry

14.1K
The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...
14.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Beyond binary classification: a pilot study of imaging-derived glioma severity modeling using T1-weighted and diffusion MRI radiomics.

Magma (New York, N.Y.)·2026
Same author

Design, 3D printing, and preclinical validation of an extraglottic ramp to facilitate blind orotracheal intubation in emergency airway management.

PloS one·2025
Same author

Physics-Informed Neural Network for Modeling the Pulmonary Artery Blood Pressure from Magnetic Resonance Images: A Reduced-Order Navier-Stokes Model.

Biomedicines·2025
Same author

AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.

Journal of cardiovascular development and disease·2025
Same author

An Explainable Fuzzy Framework for Assessing Preeclampsia Classification.

Biomedicines·2025
Same author

Air Quality Prediction Based on Singular Spectrum Analysis and Artificial Neural Networks.

Entropy (Basel, Switzerland)·2025

Related Experiment Video

Updated: Oct 2, 2025

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics
07:42

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

Published on: February 19, 2017

8.9K

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification.

César Cheuque1, Marvin Querales2, Roberto León1

  • 1Facultad de Ingeniería, Universidad Andres Bello, Viña del Mar 2531015, Chile.

Diagnostics (Basel, Switzerland)
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-stage machine learning model for accurate white blood cell classification from blood smears. The advanced system achieves high performance, aiding pathologists in immune system quality assessment.

Keywords:
deep learningmulti-level classificationmulti-source datasetswhite blood cells classification

More Related Videos

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation
10:27

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Published on: June 4, 2015

12.0K
Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

11.7K

Related Experiment Videos

Last Updated: Oct 2, 2025

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics
07:42

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

Published on: February 19, 2017

8.9K
Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation
10:27

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Published on: June 4, 2015

12.0K
Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

11.7K

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Artificial Intelligence in Medicine

Background:

  • Accurate white blood cell evaluation is crucial for immune system assessment.
  • Current methods rely heavily on pathologist expertise, and existing machine learning tools offer limited classification levels.
  • Automating this process can improve diagnostic efficiency and consistency.

Purpose of the Study:

  • To develop and evaluate a novel two-stage hybrid multi-level scheme for classifying white blood cells.
  • To differentiate between mononuclear (lymphocytes, monocytes) and polymorphonuclear (segmented neutrophils, eosinophils) cell groups.
  • To enhance computer-aided diagnosis (CAD) tools for pathologists.

Main Methods:

  • A Faster R-CNN network was employed for initial region identification and separation of mononuclear from polymorphonuclear cells.
  • Two parallel convolutional neural networks utilizing the MobileNet architecture were used for subclass recognition in the second stage.
  • Monte Carlo cross-validation was applied to assess model performance.

Main Results:

  • The proposed model achieved a high performance metric of approximately 98.4% across accuracy, recall, precision, and F1-score.
  • The two-stage approach effectively classified four key white blood cell groups.
  • The system demonstrated robust performance in identifying and subclassifying white blood cells.

Conclusions:

  • The developed two-stage hybrid model offers a significant advancement for automated white blood cell analysis.
  • This approach provides a reliable alternative for computer-aided diagnosis (CAD) systems.
  • The model can effectively support pathologists in clinical laboratories for blood smear image assessment.