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Disorders of Leukocytes01:27

Disorders of Leukocytes

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Leukocyte disorders can lead to either leukopenia, characterized by an abnormally low leukocyte count, or leukocytosis, marked by a very high leukocyte number.
Leukopenia may result from bone marrow disorders, autoimmune diseases, and infectious diseases. For example, conditions such as multiple myeloma and aplastic anemia can impair the bone marrow's ability to produce adequate leukocytes. Similarly, autoimmune diseases like lupus and viral infections such as HIV can prompt the immune...
1.7K
Classification of Leukocytes01:30

Classification of Leukocytes

4.6K
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...
4.6K

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Related Experiment Video

Updated: Dec 21, 2025

Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia
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Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia

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Expert knowledge for the recognition of leukemic cells.

Rocio Ochoa-Montiel, Gustavo Olague, Humberto Sossa

    Applied Optics
    |May 14, 2020
    PubMed
    Summary

    Expert knowledge extraction significantly improves leukemic cell recognition using traditional methods, achieving 99.63% accuracy. This approach is computationally efficient and explainable, outperforming deep learning benchmarks.

    Area of Science:

    • * Medical image analysis
    • * Hematology
    • * Computational pathology

    Background:

    • * Visual analysis of microscopic images is crucial for diagnosing hematological disorders.
    • * Current image recognition techniques are essential for accurate blood tissue identification.
    • * Expert knowledge integration can enhance diagnostic accuracy in pathology.

    Purpose of the Study:

    • * To develop a method for extracting expert knowledge from blood cell images.
    • * To classify healthy versus leukemic cells using extracted expert knowledge.
    • * To compare the proposed method against deep learning approaches.

    Main Methods:

    • * Employed Gaussian mixtures, evolutionary computing, and image processing for knowledge extraction.
    • * Utilized support vector machines and multilayer perceptrons for cell classification.

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    Last Updated: Dec 21, 2025

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  • * Implemented convolutional neural networks as a benchmark for performance comparison.

    • Main Results:

    • * Achieved 99.63% accuracy in blood cell recognition using the proposed method.
    • * Convolutional neural networks achieved an average accuracy of 97.74%.
    • * The developed approach demonstrated minimal computational effort and explainability.

    Conclusions:

    • * Expert knowledge-driven pattern recognition is highly effective for leukemic cell identification.
    • * Traditional methods, when enhanced with expert knowledge, can match or exceed deep learning performance.
    • * The proposed methodology offers an efficient and interpretable alternative for hematological image analysis.