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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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Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation

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Published on: November 3, 2018

Frequency-Domain Object Detection Network for Leukemia Diagnosis in Bone Marrow Microscopy.

Liye Mei^1,2,3, Xiaofang Song², Hui Shen⁴

¹Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, Hubei University of Technology, Wuhan, China.

Microscopy Research and Technique

|October 8, 2025

Summary

This study introduces an AI framework using frequency analysis of bone marrow images to improve leukemia diagnosis. The AI accurately distinguishes between acute lymphocytic leukemia (ALL) and chronic lymphocytic leukemia (CLL).

Keywords:

acute lymphoblastic leukemia bone marrow microscopy chronic lymphocytic leukemia frequency domain

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Hematology
Medical Imaging
Artificial Intelligence

Background:

Leukemia diagnosis is challenging due to morphological heterogeneity in optical microscopy.
Accurate identification of leukemic cells is crucial for effective treatment.

Purpose of the Study:

To develop a frequency-domain guided object detection framework for enhanced leukemia diagnosis.
To improve the accuracy of distinguishing between different types of leukemia using microscopic images.

Main Methods:

Utilized frequency-based image enhancement techniques on high-resolution bone marrow images.
Implemented refined feature integration combining spatial and frequency information.
Developed an object detection framework for leukemic cell classification.

Main Results:

Achieved high precision in distinguishing acute lymphocytic leukemia (ALL) with an average precision of 89.7%.
Achieved high precision in distinguishing chronic lymphocytic leukemia (CLL) with an average precision of 95.6%.
Demonstrated the framework's ability to capture critical fine-grained details and semantic patterns.

Conclusions:

The proposed AI framework significantly enhances diagnostic accuracy for leukemia classification.
Integrating artificial intelligence with optical microscopy offers a valuable tool for hematologic malignancy diagnosis.
Frequency-domain analysis combined with object detection shows promise for improving leukemia identification.