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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.
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Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier.

Tawfeeq Shawly1, Ahmed A Alsheikhy2

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A novel deep learning method accurately detects acute lymphocytic leukemia (ALL) and acute myelogenous leukemia (AML) from blood cell images. This automated approach achieves over 98% accuracy, aiding in early cancer diagnosis and patient care.

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Area of Science:

  • Medical Diagnostics
  • Computational Biology
  • Oncology

Background:

  • Leukemia, a common childhood cancer, includes acute lymphocytic leukemia (ALL) and acute myelogenous leukemia (AML).
  • Current leukemia detection relies on manual blood smear analysis by hematologists, which can be time-consuming.
  • Abnormal white blood cells produced in leukemia impair immune function, necessitating accurate and timely diagnosis.

Purpose of the Study:

  • To develop and propose an automated deep learning-based method for detecting ALL and AML.
  • To determine the severity of detected leukemia types.
  • To provide patient recommendations based on diagnostic outcomes.

Main Methods:

  • Utilized image segmentation and a convolutional neural network (CNN) tool, AlexNet, for leukemia detection.
  • Employed the C-NMC_Leukemia dataset, comprising 15,000 images, for method validation.
  • Performed comparative analysis against existing literature methods, evaluating feature extraction, classifiers, accuracy, precision, and recall.

Main Results:

  • The proposed deep learning approach achieved over 98% accuracy in detecting ALL and AML.
  • The method demonstrated superior performance compared to other existing methods in comparative analyses.
  • A confusion matrix confirmed the correctness and reliability of the developed approach.

Conclusions:

  • The developed deep learning classifier effectively detects and differentiates between ALL and AML.
  • This automated method offers a highly accurate and efficient alternative to manual blood smear analysis.
  • The system's ability to recommend next steps enhances its clinical utility for patient management.