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Related Experiment Video

Updated: Sep 19, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Employing transfer learning for breast cancer detection using deep learning models.

Frimpong Twum1, Charlyne Carol Eyram Ahiable1, Stephen Opoku Oppong1

  • 1Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

PLOS Digital Health
|June 16, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel deep learning model for breast cancer detection, achieving up to 95.5% accuracy. The model utilizes pretrained deep learning networks to enhance early and accurate diagnosis, improving patient outcomes.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a significant global health issue.
  • Early and accurate detection is crucial for improving patient outcomes.
  • Traditional diagnostic methods have limitations in accuracy.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for breast cancer detection.
  • To compare the performance of four pretrained deep learning models (Mobilenetv2, Inceptionv3, ResNet50, VGG16) as feature extractors.
  • To assess the effectiveness of supervised learning models when combined with these deep learning features.

Main Methods:

  • Utilized the BUSI dataset for training and testing.
  • Employed four pretrained deep learning models (Mobilenetv2, Inceptionv3, ResNet50, VGG16) as feature extractors.

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  • Implemented transfer learning by freezing top layers and adding new ones, with a GlobalAveragePooling2D layer.
  • Fed extracted features into supervised learning models like Logistic Regression and Light Gradient Boosting Machine.

    • Main Results:

    • ResNet50 achieved the highest accuracy of 95.5% after transfer learning.
    • Inceptionv3, VGG16, and Mobilenetv2 achieved accuracies of 92.5%, 86.5%, and 84%, respectively.
    • Logistic Regression and Light Gradient Boosting Machine were identified as top-performing classifiers when combined with deep learning features.

    Conclusions:

    • The proposed novel deep learning model demonstrates high accuracy in breast cancer detection.
    • Transfer learning with pretrained models like ResNet50 significantly enhances diagnostic accuracy.
    • This approach offers a promising tool for improving early and accurate breast cancer diagnosis.