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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...

Classification of Epithelial Tissues: Overview

Classification of Epithelial Tissues: Overview

Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...

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Updated: Aug 28, 2025

A Detailed Protocol for Characterizing the Murine C1498 Cell Line and its Associated Leukemia Mouse Model

A Detailed Protocol for Characterizing the Murine C1498 Cell Line and its Associated Leukemia Mouse Model

Published on: October 14, 2016

Leukemia segmentation and classification: A comprehensive survey.

Saba Saleem¹, Javaria Amin², Muhammad Sharif¹

¹Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.

Computers in Biology and Medicine

|September 20, 2022

Summary

This summary is machine-generated.

This study explores deep learning for accurate leukemia detection from blood smear images. These advanced methods aim to improve early diagnosis and reduce mortality rates associated with this blood cancer.

Keywords:

Classification Features extraction Features selection Leukemia Leukocytes Segmentation

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Published on: March 26, 2018

Area of Science:

Hematology
Oncology
Medical Imaging
Artificial Intelligence

Background:

Leukemia, a cancer of white blood cells (WBCs), is characterized by uncontrolled proliferation of immature WBCs in bone marrow.
Current diagnostic methods, including manual microscopy and cell counting, are time-consuming, labor-intensive, and prone to errors.
Late detection and limitations of existing techniques contribute to a significant mortality rate from leukemia.

Purpose of the Study:

To review and discuss recent advancements in deep learning methodologies for leukemia detection.
To highlight the challenges associated with implementing deep learning in leukemia diagnosis.
To explore the potential of deep learning in improving the accuracy and efficiency of leukemia detection from microscopic blood smear images.

Main Methods:

The study focuses on deep learning approaches applied to the analysis of microscopic blood smear images.
Key stages discussed include image pre-processing, segmentation, feature extraction, and classification using deep learning models.
Comparison with traditional methods like fluorescence-based cell sorting and hemocytometer counts, noting their inaccuracies.

Main Results:

Deep learning methodologies offer enhanced accuracy in analyzing blood smear images for leukemia detection.
These methods can potentially overcome the limitations of manual examination and traditional automated techniques.
The discussed approaches provide a framework for more reliable and efficient leukemia diagnosis.

Conclusions:

Deep learning presents a promising avenue for improving the accuracy and efficiency of leukemia detection.
Further research and development in deep learning are crucial for overcoming current diagnostic challenges.
Adoption of these advanced techniques could lead to earlier diagnosis and improved patient outcomes in leukemia.