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Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature

Somnath Chatterjee1, Shreya Biswas2, Arindam Majee2

  • 1Future Institute of Engineering and Management, Kolkata, West Bengal, India.

Computers in Biology and Medicine
|November 20, 2021
PubMed
Summary

This study introduces a novel two-stage deep learning model for early breast cancer detection using thermographic images. The model achieves 100% diagnostic accuracy while significantly reducing the number of features required.

Keywords:
Breast cancer detectionDeep learningDragonfly algorithmEvolutionary AlgorithmsFeature selectionFractional order calculusMedical Image analysisThermography image

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of death in women, with increasing incidence rates.
  • Early detection significantly improves survival rates and treatment outcomes.
  • Deep learning in medical imaging offers promising avenues for advanced diagnostic tools.

Purpose of the Study:

  • To develop and evaluate a two-stage model for accurate breast cancer detection using thermographic images.
  • To enhance feature selection efficiency in deep learning models for medical diagnostics.
  • To improve the diagnostic accuracy and reduce feature dimensionality in breast cancer screening.

Main Methods:

  • A two-stage framework combining deep learning (VGG16) for feature extraction and a meta-heuristic Dragonfly Algorithm (DA) for feature selection.
  • Implementation of a memory-based DA incorporating the Grunwald-Letnikov (GL) method to optimize feature selection.
  • Evaluation of the proposed model on the publicly available DMR-IR dataset for breast thermography.

Main Results:

  • The proposed two-stage model achieved 100% diagnostic accuracy on the DMR-IR dataset.
  • The model successfully filtered out non-essential features, reducing feature count by 82% compared to using VGG16 alone.
  • The enhanced Dragonfly Algorithm improved the efficiency of optimal feature subset selection.

Conclusions:

  • The developed two-stage model demonstrates high efficacy and efficiency for breast cancer detection via thermography.
  • This approach offers a significant reduction in feature dimensionality without compromising diagnostic accuracy.
  • The integration of deep learning and optimized meta-heuristic algorithms presents a powerful tool for early breast cancer diagnosis.