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Breast cancer inter-image dissimilarity by feature optimization: An application of novel flea optimization algorithm.

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This study introduces an optimized ResNet-50 model for breast cancer detection, achieving high accuracy in classifying biopsy slides. The AI tool aids experts in early breast cancer diagnosis.

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Area of Science:

  • Medical Imaging Analysis
  • Computational Pathology
  • Artificial Intelligence in Oncology

Background:

  • Breast cancer remains a significant global health concern, causing substantial mortality.
  • Continued advancements are crucial for improving early detection and patient outcomes.

Purpose of the Study:

  • To develop an efficient and accurate deep learning model for breast cancer diagnosis using histopathology images.
  • To enhance the classification performance of breast cancer detection systems.

Main Methods:

  • A modified ResNet-50 architecture (146 layers) was employed for feature extraction from the BreakHis dataset.
  • A novel Flea Optimization Algorithm was utilized for global feature extraction and inter-image dissimilarity evaluation.
  • The model's performance was statistically analyzed using MCC, Cohen's Kappa, and t-tests.

Main Results:

  • The proposed model demonstrated superior performance compared to existing methods like DenseNet and VGG.
  • High classification accuracies were achieved across different magnifications: 99.20% (40×), 99.62% (100×), 99.50% (200×), and 99.34% (400×).

Conclusions:

  • The developed AI framework offers a promising alternative for early breast cancer diagnosis.
  • Implementation on real hardware suggests practical utility for healthcare professionals.