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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...

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Updated: Aug 20, 2025

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay

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Published on: September 16, 2012

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification.

Channabasava Chola¹, Abdullah Y Muaad², Md Belal Bin Heyat^3,4,5

¹Department of Electronics and Information Convergence Engineering, College of Electronics and Information, Kyung Hee University, Suwon-si 17104, Republic of Korea.

Diagnostics (Basel, Switzerland)

|November 26, 2022

Summary

This summary is machine-generated.

BCNet, an AI deep learning framework, accurately identifies eight blood cell types. It achieves 98.51% accuracy and outperforms other models in speed, enhancing healthcare diagnostics.

Keywords:

blood cell deep transfer learning multi-class identification new BCNet framework verification and validation

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Published on: November 19, 2018

Area of Science:

Hematology and Medical Diagnostics
Artificial Intelligence in Healthcare
Computational Biology and Bioinformatics

Background:

Accurate identification of blood cells is crucial for assessing a patient's health status and managing infection risks.
Manual blood cell identification is time-consuming and prone to human error, necessitating automated solutions.
Deep learning (DL) offers potential for rapid and precise automated analysis of cellular images.

Purpose of the Study:

To introduce BCNet, a novel artificial intelligence (AI)-based deep learning (DL) framework for automated blood cell identification.
To evaluate the performance and efficiency of BCNet in classifying eight distinct types of blood cells.
To compare BCNet's capabilities against existing state-of-the-art DL models for blood cell analysis.

Main Methods:

Developed BCNet using a convolutional neural network (CNN) architecture with transfer learning capabilities.
Conducted five-fold cross-validation experiments to assess model dependability and viability.
Tested BCNet with ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD) optimizers, comparing results with DenseNet, ResNet, Inception, and MobileNet.

Main Results:

BCNet demonstrated superior classification performance with ADAM and RMSP optimizers, achieving a peak accuracy of 98.51% and an F1-score of 96.24% with RMSP.
BCNet improved prediction accuracy by 1.94% (ADAM), 3.33% (RMSP), and 1.65% (SGD) compared to baseline models.
BCNet exhibited significantly faster testing times per image (10.98, 4.26, 2.03, and 0.21 msec faster than DenseNet, ResNet, Inception, and MobileNet, respectively).

Conclusions:

The BCNet framework provides a dependable and efficient AI-driven solution for automated blood cell identification.
BCNet's high accuracy and rapid processing speed offer significant advantages over existing DL models.
The proposed BCNet model shows strong potential for advancing diagnostic capabilities in healthcare facilities.