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Related Concept Videos

Classification of Leukocytes01:30

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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Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

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An AI-based automatic leukemia classification system utilizing dimensional Archimedes optimization.

Warda M Shaban1,2

  • 1Department Communication and Electronics Engineering, Nile Higher Institute for Engineering and Technology, Mansoura, Egypt. warda_mohammed@nilehi.edu.eg.

Scientific Reports
|May 16, 2025
PubMed
Summary
This summary is machine-generated.

A new Artificial Intelligence (AI) system, the Leukemia Classification System (LCS), accurately detects leukemia by analyzing blood cell images. This AI system aids in early diagnosis, improving patient outcomes.

Keywords:
ClassificationFeature selectionLeukemiaMachine learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Hematology

Background:

  • Leukemia is a prevalent blood cancer characterized by uncontrolled white blood cell proliferation.
  • This proliferation impairs bone marrow function, affecting platelet and red blood cell production, and can damage organs.
  • Early detection and classification of leukemia are critical for effective treatment and patient survival.

Purpose of the Study:

  • To propose a novel Artificial Intelligence (AI) system for accurate and early leukemia detection and classification.
  • To develop a robust multi-stage system integrating image processing, segmentation, feature extraction, and classification.

Main Methods:

  • The Leukemia Classification System (LCS) employs a five-stage pipeline: Image Processing (IPS), Image Segmentation (ISS), Feature Extraction (FES), Feature Selection (FSS), and Classification (CS).
  • Texture and morphological features are extracted and refined using the Dimensional Archimedes Optimization Algorithm (DAOA) for feature selection.
  • The DAOA, incorporating Dimensional Learning Strategy (DLS), enhances convergence precision and efficiency.

Main Results:

  • The proposed LCS demonstrated superior performance compared to existing methods in leukemia classification.
  • The integration of DAOA for feature selection significantly improved the accuracy and efficiency of the classification process.

Conclusions:

  • The developed AI-based Leukemia Classification System (LCS) offers a promising tool for early and accurate leukemia diagnosis.
  • The novel feature selection method, DAOA, effectively identifies critical features, enhancing classification performance.