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

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

Published on: November 19, 2018

Automatic detection and classification of leukocytes using convolutional neural networks.

Jianwei Zhao¹, Minshu Zhang¹, Zhenghua Zhou¹

¹Department of Applied Mathematics, College of Science, China Jiliang University, Hangzhou, 310018, Zhejiang Province, People's Republic of China.

Medical & Biological Engineering & Computing

|November 9, 2016

Summary

This study presents an automated system for detecting and classifying white blood cells (WBCs) in blood images. The method improves diagnostic accuracy and efficiency compared to manual analysis.

Keywords:

Classification Convolutional neural networks Detection Random forest White blood cell

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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

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Area of Science:

Hematology
Medical Imaging
Computer Vision

Background:

Manual white blood cell (WBC) analysis is slow and subjective.
Existing automated methods often focus on detection or classification independently.
Accurate WBC detection and classification are crucial for disease diagnosis.

Purpose of the Study:

To develop an integrated, automated system for WBC detection and classification.
To improve the speed and accuracy of WBC analysis in peripheral blood images.
To address the limitations of manual blood cell morphological analysis.

Main Methods:

A novel algorithm for WBC detection using color and morphological operations.
Classification of eosinophils and basophils using PRICoLBP features and SVM.
High-level feature extraction with Convolutional Neural Networks (CNNs) and classification using Random Forest for neutrophils, monocytes, and lymphocytes.

Main Results:

The proposed detection method demonstrated superior performance and reduced time compared to the iterative threshold method.
The classification approach achieved high accuracy, outperforming other existing methods.
The system successfully integrates detection and classification of five types of WBCs.

Conclusions:

The developed automated system offers a more efficient and accurate alternative to manual WBC analysis.
This integrated approach advances automated hematological diagnostics.
The system shows significant potential for clinical applications in disease diagnosis.