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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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A computer-assisted human peripheral blood leukocyte image classification method based on Siamese network.

Yapin Wang¹, Yiping Cao²

¹Department of Opto-electronics, Sichuan University, Chengdu, 610064, China.

Medical & Biological Engineering & Computing

|May 18, 2020

Summary

This summary is machine-generated.

This study introduces a novel computer-assisted method for classifying human peripheral blood leukocytes using a Siamese network. The approach achieves high accuracy in identifying leukocyte types, improving diagnostic potential.

Keywords:

Data augmentation Leukocyte classification Logistic regression Siamese network Similarity metric

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Area of Science:

Medical Imaging
Computational Biology
Artificial Intelligence

Background:

Accurate classification of human peripheral blood leukocytes is crucial for diagnosing various medical conditions.
Traditional methods can be time-consuming and prone to human error.
Automated systems offer potential for faster and more consistent analysis.

Purpose of the Study:

To develop and evaluate a computer-assisted method for classifying five types of human peripheral blood leukocytes.
To leverage a Siamese network architecture for enhanced feature learning and similarity metric calculation.
To improve the accuracy and efficiency of leukocyte image classification.

Main Methods:

A Siamese network comprising two identical convolutional neural network (CNN) sub-networks was designed for leukocyte classification.
A logistic regression model was integrated for the final classification task.
A data augmentation technique was employed to balance genuine and impostor pairs for training the Siamese network.
Hematologist-selected typical samples were used to create genuine and impostor pairs for training.

Main Results:

The proposed Siamese network effectively learned distinguishing features and a similarity metric for leukocytes.
Data augmentation successfully enriched the dataset, addressing the imbalance between genuine and impostor pairs.
The method demonstrated a high average testing accuracy of 98.8% in classifying leukocyte images.
The Siamese network successfully minimized similarity metrics for same-category pairs and maximized them for different-category pairs.

Conclusions:

The developed computer-assisted method using a Siamese network provides a highly accurate approach for human peripheral blood leukocyte classification.
The method shows significant potential for integration into clinical diagnostic workflows.
The Siamese network architecture is effective for learning robust features and similarity metrics in medical image analysis.