Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

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

Sort by

Same author

Computational intelligence using nailfold videocapillaroscopy for the prediction of carotid intima-media thickness in rheumatoid arthritis: a cohort-based study.

Rheumatology international·2026

Same author

Breast Cancer Biomarker Discovery Using an Enhanced Quantum-Based Avian Navigation Optimizer and Ensemble Learning Model.

IEEE transactions on computational biology and bioinformatics·2026

Same author

Hybrid machine learning approach for predicting compressive strength of sustainable concrete incorporating palm oil fuel ash.

Scientific reports·2026

Same author

Attention-aided hybrid transformer network for oral squamous cell carcinoma classification using histopathology images.

Frontiers in artificial intelligence·2026

Same author

Hybrid neutrosophic enhanced MobileNetV2 model for leukemia blood cell classification.

Frontiers in artificial intelligence·2026

Same author

Advanced kidney mass segmentation using VHUCS-Net with protuberance detection network.

Frontiers in artificial intelligence·2026

Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026

Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026

Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026

Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026

Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026

Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2026

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Published on: March 26, 2018

An attention-based deep learning for acute lymphoblastic leukemia classification.

Malathy Jawahar¹, L Jani Anbarasi², Sathiya Narayanan³

¹Leather Process Technology Division, CSIR-Central Leather Research Institute, Chennai, India.

Scientific Reports

|July 29, 2024

Summary

This summary is machine-generated.

A new Deep Dilated Residual Convolutional Neural Network (DDRNet) accurately classifies blood cells for early Acute Lymphoblastic Leukemia (ALL) detection. This AI model achieves high accuracy, aiding hematologists in diagnosis and reducing workload.

Keywords:

Attention layer Computer-aided diagnosis Convolutional neural network Deep learning models Leukemia

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia

Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia

Published on: February 21, 2021

Related Experiment Videos

Last Updated: Jun 24, 2026

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Published on: March 26, 2018

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia

Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia

Published on: February 21, 2021

Area of Science:

Medical Imaging
Artificial Intelligence
Hematology

Background:

Acute Lymphoblastic Leukemia (ALL) is a bone marrow malignancy characterized by overproduction of immature cells.
ALL diagnoses in the U.S. approach 6500 annually, representing a significant portion of pediatric cancers.
Computer-assisted diagnosis (CAD) systems are increasingly vital for hematologists to manage data and improve diagnostic accuracy.

Purpose of the Study:

To introduce a novel Deep Dilated Residual Convolutional Neural Network (DDRNet) for automated blood cell classification.
To enhance feature extraction and classification accuracy for early detection of Acute Lymphoblastic Leukemia (ALL).
To address challenges in CAD systems, such as vanishing gradients and feature discrimination.

Main Methods:

The study developed a DDRNet model incorporating Deep Residual Dilated Blocks (DRDB), Global and Local Feature Enhancement Blocks (GLFEB), and Channel and Spatial Attention Blocks (CSAB).
The model utilizes Tanh and sigmoid activation functions for non-linearity and feature concentration.
A Kaggle dataset of 16,249 blood cell images, categorized into four classes, was used for training (80%) and testing (20%).

Main Results:

The DDRNet model achieved a high training accuracy of 99.86% and a testing accuracy of 91.98%.
The model demonstrated a significant F1 score of 0.96, indicating robust classification performance.
The integration of DRDB, GLFEB, and CSAB blocks enhanced feature discrimination with minimal computational complexity.

Conclusions:

The DDRNet model offers superior performance in multi-class blood cell classification compared to existing methods.
The proposed architecture effectively addresses challenges in feature extraction and classification for ALL detection.
DDRNet shows promise as an advanced CAD tool for hematologists, improving diagnostic efficiency and accuracy.