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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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Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification.

Essam H Houssein1, Osama Mohamed2, Nagwan Abdel Samee3

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This study introduces a deep learning approach using DenseNet-161 and cyclical learning rates for accurate white blood cell (WBC) classification, achieving 99.8% accuracy in leukemia detection. This method enhances diagnostic capabilities for hematological disorders.

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Area of Science:

  • Hematology
  • Medical Imaging
  • Computational Biology

Background:

  • Accurate white blood cell (WBC) classification is crucial for diagnosing hematological disorders like leukemia.
  • Traditional methods for blood cell analysis can be time-consuming and subjective.
  • Deep learning (DL) offers advanced solutions for automated blood cell classification.

Purpose of the Study:

  • To develop and implement an end-to-end computer-aided diagnosis (CAD) system for leukocyte classification.
  • To enhance the accuracy and efficiency of WBC classification using deep learning techniques.
  • To improve the detection and diagnosis of leukemia and other blood-related diseases.

Main Methods:

  • Utilized a dataset of microscopic blood cell images sourced from GitHub.
  • Implemented an integrated system involving image preprocessing, segmentation, feature extraction, and WBC classification.
  • Employed DenseNet-161 combined with cyclical learning rate (CLR) and the one-cycle technique for rapid hyperparameter optimization and improved training performance.

Main Results:

  • The developed system achieved 100% accuracy on the training dataset.
  • The system demonstrated a high accuracy of 99.8% on the testing dataset.
  • The combination of DenseNet-161 and CLR accelerated hyperparameter optimization and boosted training performance.

Conclusions:

  • A novel deep learning-based technique using DenseNet and a one-fit-cycle policy was successfully developed for WBC classification in leukemia detection.
  • The proposed method significantly outperforms existing state-of-the-art approaches in terms of accuracy.
  • This advanced CAD system holds promise for more accurate and efficient diagnosis of hematological malignancies.