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Csec-net: a novel deep features fusion and entropy-controlled firefly feature selection framework for leukemia

Sarmad Maqsood1,2, Robertas Damaševičius1, Rytis Maskeliūnas1

  • 1Centre of Real Time Computer Systems, Faculty of Informatics, Kaunas University of Technology, LT-51386 Kaunas, Lithuania.

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|December 31, 2024
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Summary

This study introduces a novel deep learning approach for computer-aided leukemia diagnosis, achieving high accuracy in classifying blood cell images. The method enhances diagnostic efficiency for this life-threatening cancer.

Keywords:
ClassificationDeep featuresDeep learningLeukemiaMicroscopyTransfer learning

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Oncology

Background:

  • Leukemia diagnosis currently relies on manual microscopic image analysis.
  • Machine learning, particularly deep learning, offers advanced solutions for image classification.
  • There is a need for automated, accurate diagnostic tools for leukemia.

Purpose of the Study:

  • To develop and evaluate deep learning models for computer-aided leukemia diagnosis.
  • To improve the accuracy and efficiency of leukemia detection from blood cell images.
  • To establish a robust automated system for classifying leukemia subtypes.

Main Methods:

  • Preprocessing of leukemia dataset images.
  • Transfer learning using five pre-trained convolutional neural network models (MobileNetV2, EfficientNetB0, ConvNeXt-V2, EfficientNetV2, DarkNet-19).
  • Fusion of deep features via convolutional sparse image decomposition, followed by entropy-controlled firefly feature selection and multi-class support vector machine classification.

Main Results:

  • The proposed deep learning algorithm achieved high accuracies across four datasets: ALLID_B1 (99.64%), ALLID_B2 (98.96%), C_NMC 2019 (96.67%), and ASH (98.89%).
  • The method demonstrated superior performance compared to existing approaches in leukemia image classification.
  • Successful application to 15,562 images, confirming robustness and scalability.

Conclusions:

  • The developed deep learning framework provides an effective and accurate method for computer-aided leukemia diagnosis.
  • This approach has the potential to significantly aid clinicians in the early and precise detection of leukemia.
  • The study highlights the power of deep learning in advancing cancer diagnostics.