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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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Updated: Jun 29, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Published on: September 25, 2021

DLBCNet: A Deep Learning Network for Classifying Blood Cells.

Ziquan Zhu¹, Zeyu Ren¹, Siyuan Lu¹

¹School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK.

Big Data and Cognitive Computing

|April 1, 2024

Summary

This summary is machine-generated.

This study introduces DLBCNet, a novel deep learning network for multi-class blood cell classification. The model achieves high accuracy, demonstrating improved blood cell analysis performance.

Keywords:

ResNet50 blood cells generative adversarial networks randomized neural network

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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
Medical Imaging
Artificial Intelligence

Background:

Blood analysis provides crucial health insights.
Deep learning (DL) models are increasingly used for automated blood cell analysis.
Existing DL models for blood cell analysis have limitations.

Purpose of the Study:

To propose a novel deep learning network, DLBCNet, for multi-classification of blood cells.
To enhance the accuracy and performance of automated blood cell diagnosis.

Main Methods:

Developed DLBCNet, incorporating a blood cell generative adversarial network (BCGAN) for synthetic image generation.
Utilized a pre-trained ResNet50 as a backbone for feature extraction.
Employed an enhanced transformer recurrent network (ETRN) for improved classification.

Main Results:

Achieved an average accuracy of 95.05%.
Reported average sensitivity, precision, specificity, and F1-score of 93.25%, 97.75%, 93.72%, and 95.38%, respectively.
Demonstrated superior performance compared to existing state-of-the-art methods.

Conclusions:

DLBCNet significantly improves multi-classification performance for blood cells.
The proposed model offers a promising advancement in automated hematological analysis.
The results indicate the potential of DLBCNet for clinical applications.