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Breast Cancer Detection and Classification Empowered With Transfer Learning.

Sahar Arooj1, Atta-Ur-Rahman2, Muhammad Zubair3

  • 1Riphah School of Computing and Innovation, Riphah International University Lahore, Lahore, Pakistan.

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Summary

This study introduces a novel approach for breast cancer detection using transfer learning and a customized CNN-AlexNet model. The developed system achieved superior accuracy in identifying and classifying breast cancer from histopathology and ultrasound images compared to existing models.

Keywords:
breast cancer (BC)convolutional neural network (CNN)deep learning (DL)learning rate (LR)machine learning (ML)transfer learning (TL)

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Area of Science:

  • Oncology
  • Medical Imaging
  • Computer-Aided Diagnosis
  • Artificial Intelligence in Medicine

Background:

  • Breast cancer is a leading cause of mortality in women globally, necessitating advanced diagnostic tools.
  • Accurate and early detection of breast cancer is crucial for effective treatment and improved patient survival rates.
  • Existing computer-aided diagnosis systems for breast cancer face limitations, including dataset scarcity and model performance.

Purpose of the Study:

  • To develop an automated system for the identification and diagnosis of breast cancer using histopathology and ultrasound images.
  • To enhance the accuracy of breast cancer classification by employing transfer learning techniques.
  • To address dataset limitations by customizing a Convolutional Neural Network (CNN)-AlexNet model.

Main Methods:

  • Utilized transfer learning (TL) on three distinct datasets (A, B, C) and a modified version (A2).
  • Employed a customized CNN-AlexNet architecture, trained specifically for the characteristics of the provided datasets.
  • Integrated both histopathology and ultrasound imaging modalities for comprehensive analysis.

Main Results:

  • The proposed transfer learning-enhanced system demonstrated superior performance across all tested datasets (A, B, C, A2).
  • Achieved higher accuracy in breast cancer identification and classification compared to existing models.
  • The customized CNN-AlexNet model effectively adapted to the specific requirements of the medical imaging datasets.

Conclusions:

  • Transfer learning significantly improves the accuracy of automated breast cancer detection systems.
  • The customized CNN-AlexNet model offers a promising solution for computer-aided diagnosis of breast cancer.
  • This approach holds potential for earlier and more accurate diagnosis, leading to better patient outcomes.