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Related Experiment Video

Updated: Oct 3, 2025

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Automated Breast Cancer Detection Models Based on Transfer Learning.

Madallah Alruwaili1, Walaa Gouda1

  • 1College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

This study enhances breast cancer detection using deep learning transfer learning models. Transfer learning with ResNet50 achieved 89.5% accuracy on the MIAS dataset, improving early diagnosis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Breast cancer remains a leading cause of female mortality globally.
  • Early detection and diagnosis are crucial for improving patient outcomes.
  • Deep learning (DL) models offer potential to enhance mammography interpretation, overcoming human observer limitations.

Purpose of the Study:

  • To introduce a novel framework utilizing transfer learning for breast cancer detection in mammography.
  • To improve the accuracy and efficiency of distinguishing malignant from benign breast tumors.
  • To address challenges with small datasets in medical imaging through data augmentation.

Main Methods:

  • Implemented a transfer learning framework by fine-tuning pre-trained deep learning models.
Keywords:
Nasnet-MobileResNetbreast cancerdeep learningmammogrammedical imagingtransfer learning

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  • Employed diverse data augmentation strategies (rotation, scaling, shifting) to increase mammographic image data and prevent overfitting.
  • Evaluated the system on the Mammographic Image Analysis Society (MIAS) dataset.

    • Main Results:

    • The proposed system achieved 89.5% accuracy using the ResNet50 model.
    • The Nasnet-Mobile network achieved 70% accuracy.
    • Demonstrated the effectiveness of pre-trained classification networks for medical imaging tasks.

    Conclusions:

    • Transfer learning, particularly with models like ResNet50, is highly effective for breast cancer detection in mammography.
    • The framework shows promise for improving diagnostic accuracy, especially with limited training data.
    • Pre-trained deep learning models are efficient and suitable for medical image analysis.