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Classification of Leukocytes01:30

Classification of Leukocytes

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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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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images.

Niranjana Sampathila1, Krishnaraj Chadaga2, Neelankit Goswami1

  • 1Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.

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PubMed
Summary
This summary is machine-generated.

This study introduces an AI deep learning model to detect acute lymphoblastic leukemia (ALL) from blood smear images. The ALLNET model achieves high accuracy, aiding in early cancer detection and diagnosis.

Keywords:
acute lymphoblastic leukemia (ALL)blood smearconvolutional neural networksdeep learningwhite blood cells

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Area of Science:

  • Hematology
  • Oncology
  • Artificial Intelligence

Background:

  • Acute lymphoblastic leukemia (ALL) is a bone marrow cancer characterized by lymphocyte overproduction.
  • While common in children with good cure rates, adult ALL diagnosis at later stages has a poorer prognosis.
  • Early detection is crucial for improving treatment outcomes in ALL.

Purpose of the Study:

  • To develop an intelligent deep learning algorithm for the early detection of acute lymphoblastic leukemia (ALL).
  • To accurately screen white blood cells for leukemic cells using microscopic blood smear images.

Main Methods:

  • A deep learning approach utilizing a convolutional neural network (CNN).
  • The custom ALLNET model was trained and validated on open-source microscopic blood smear images.
  • Training was performed on Google Collaboratory using Nvidia Tesla P-100 GPU.

Main Results:

  • The ALLNET model achieved high performance metrics: 95.54% accuracy, 95.81% specificity, 95.91% sensitivity, 95.43% F1-score, and 96% precision.
  • The classifier demonstrated effective differentiation between leukemic and healthy blood cells.

Conclusions:

  • The proposed deep learning technique shows significant potential for the pre-screening of leukemia cells.
  • This method can be integrated into complete blood count (CBC) and peripheral blood tests for enhanced diagnostic capabilities.