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An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm.

Essam H Houssein1, Marwa M Emam1, Abdelmgeid A Ali1

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt.

Neural Computing & Applications
|June 14, 2022
PubMed
Summary

This study introduces a new breast cancer detection model using an improved marine predators algorithm (IMPA) with ResNet50. The IMPA-ResNet50 model significantly enhances early breast cancer classification accuracy in mammographic images.

Keywords:
Breast cancer classificationConvolutional neural networkDeep learningHyperparameters optimizationMarine predators algorithmOpposition-based learningTransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of death in women, necessitating effective early detection methods.
  • Convolutional Neural Networks (CNNs) show promise in improving tumor detection and classification in medical imaging.
  • Traditional methods for breast cancer diagnosis can be enhanced by advanced computational approaches.

Purpose of the Study:

  • To propose a novel breast cancer classification model integrating a hybridized CNN with an improved optimization algorithm and transfer learning.
  • To enhance the efficiency and accuracy of breast cancer abnormality detection for radiologists.
  • To optimize CNN hyperparameters using an advanced metaheuristic algorithm.

Main Methods:

  • A novel classification model, IMPA-ResNet50, was developed by hybridizing a pretrained ResNet50 (residual network) with an improved marine predators algorithm (IMPA).
  • The IMPA was created by enhancing the original marine predators algorithm (MPA) with an opposition-based learning strategy to refine hyperparameter optimization for the CNN.
  • The model was evaluated on two mammographic datasets: the Mammographic Image Analysis Society (MIAS) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM).

Main Results:

  • The IMPA-ResNet50 model achieved high performance, with accuracy, sensitivity, and specificity rates exceeding 98% on both datasets.
  • Specifically, on the CBIS-DDSM dataset, the model reached 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity.
  • On the MIAS dataset, the model achieved 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity, outperforming other state-of-the-art methods.

Conclusions:

  • The proposed IMPA-ResNet50 model demonstrates superior performance in breast cancer classification compared to existing state-of-the-art approaches.
  • The integration of IMPA for hyperparameter optimization significantly improves the diagnostic capabilities of the ResNet50 architecture for mammographic analysis.
  • This advanced model offers a promising tool for radiologists to improve early breast cancer detection and classification efficiency.