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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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Acute leukemia classification by ensemble particle swarm model selection.

Hugo Jair Escalante¹, Manuel Montes-y-Gómez, Jesús A González

¹National Institute of Astrophysics, Optics and Electronics, Department of Computational Sciences, Luis Enrique Erro # 1, Tonantzintla, Puebla 72840, Mexico. hugojair@inaoep.mx

Artificial Intelligence in Medicine

|April 19, 2012

Summary

This study introduces an automated method for classifying acute leukemia using bone marrow images, achieving high accuracy and offering a cost-effective alternative for developing countries.

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Area of Science:

Medical diagnostics
Machine learning in healthcare
Hematology

Background:

Acute leukemia diagnosis requires precise classification for effective treatment.
Current advanced diagnostic methods are costly and inaccessible in many regions.
Morphological analysis of bone marrow images presents a potential alternative.

Purpose of the Study:

To evaluate an automated machine learning approach for acute leukemia classification using bone marrow morphology.
To develop a cost-effective and accessible diagnostic tool for acute leukemia subtypes.

Main Methods:

Utilized ensemble particle swarm model selection (EPSMS), an automated tool for selecting classification models.
Applied EPSMS to heterogeneous classification models for acute leukemia classification.
Extracted features from bone marrow images for classification tasks.

Main Results:

EPSMS significantly outperformed manually designed classifiers on real-world acute leukemia data.
Achieved classification accuracies of 97.68% for two-type and 94.21% for multi-type problems.
Demonstrated consistent performance improvements across various classification tasks and feature sets without user intervention.

Conclusions:

Automated morphological classification of acute leukemia using EPSMS offers a viable alternative to expensive diagnostic methods, particularly in developing countries.
EPSMS effectively constructs ensemble classifiers for acute leukemia with minimal user input.
The EPSMS methodology shows promise for application in other medical classification challenges.