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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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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

Divine Senanu Ametefe¹, Suzi Seroja Sarnin¹, Darmawaty Mohd Ali¹

¹Wireless Communication Technology Group, College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.

International Journal of Laboratory Hematology

|May 10, 2024

Summary

This study introduces ALLDet, an AI tool using EfficientNetB3 for accurate acute lymphoblastic leukemia (ALL) detection from cell images. It significantly improves upon manual methods for faster, more reliable diagnosis.

Keywords:

Chan‐Vese model active contours acute lymphoblastic leukaemia (ALL)blast cell deep transfer learning

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Last Updated: May 2, 2026

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Artificial Intelligence in Medicine
Computational Hematology
Medical Diagnostics

Background:

Acute lymphoblastic leukemia (ALL) diagnosis relies on manual microscopy, which is labor-intensive and prone to human error.
Distinguishing leukemic cells from normal cells requires specialized expertise and can be challenging.

Purpose of the Study:

To develop an automated system for accurate ALL detection using deep learning.
To enhance the precision of leukemia diagnosis by overcoming limitations of manual methods.

Main Methods:

Utilized deep transfer learning with nine CNN models, including EfficientNetB3, for ALL classification.
Employed Chan-Vese model-based segmentation for precise isolation of White Blood Cell (WBC) nuclei.

Main Results:

EfficientNetB3 achieved high performance with 98.5% recall specificity, 95.86% precision, and 97.13% overall accuracy.
The Chan-Vese segmentation effectively handled irregular blast cell shapes and noise, crucial for accurate analysis.

Conclusions:

The ALLDet classifier with EfficientNetB3 and advanced segmentation represents a significant advancement in ALL detection.
This AI-driven approach promises to improve patient care through timely and precise diagnoses.
The study paves the way for further AI integration in medical diagnostics.