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Related Experiment Video

Updated: Oct 3, 2025

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
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ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning.

Deepraj Chowdhury1, Anik Das2, Ajoy Dey3

  • 1Department of Electronics and Communication, International Institute of Information Technology, Naya Raipur 493661, India.

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

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This study enhances early breast cancer detection using deep learning. A ResNet101 transfer learning model achieved 99.58% accuracy, improving diagnosis for better patient outcomes.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Improper diagnosis and treatment of breast cancer lead to significant mortality.
  • Deep learning shows promise in breast cancer detection but requires further optimization.
  • Transfer learning offers a pathway to improve the efficiency and accuracy of these methods.

Purpose of the Study:

  • To enhance the accuracy and efficiency of early breast cancer detection.
  • To leverage transfer learning with Convolutional Neural Networks (CNNs) for improved diagnostic capabilities.
  • To develop a robust framework for early breast cancer identification.

Main Methods:

  • Utilizing a pre-trained ResNet101 model, a deep learning architecture, for transfer learning.
  • Employing the ImageNet dataset to initialize model weights, avoiding training from scratch.
Keywords:
artificial intelligencebreast cancerdeep learningnoninvasive detectiontransfer learning

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  • Integrating Convolutional Neural Network (CNN) principles with transfer learning techniques.

    • Main Results:

    • The proposed ResNet101-based transfer learning framework achieved a high classification accuracy of 99.58%.
    • Extensive experiments and hyperparameter tuning were conducted to optimize performance.
    • The model demonstrated significant potential for accurate breast cancer classification.

    Conclusions:

    • The developed framework offers a highly accurate and efficient tool for early breast cancer detection.
    • Transfer learning significantly boosts the performance of deep learning models in this domain.
    • This approach can serve as a valuable aid for clinicians and improve patient outcomes.