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Transformer-inspired training principles based breast cancer prediction: combining EfficientNetB0 and ResNet50.

Tariq Shahzad1, Tehseen Mazhar2,3, Sheikh Muhammad Saqib4

  • 1Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa. tariqshahzadd@gmail.com.

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|April 18, 2025
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Summary

A new hybrid deep learning model combining EfficientNetB0 and ResNet50 significantly improves breast cancer diagnosis from histopathology images. This accurate tool enhances early detection, addressing critical needs in cancer screening.

Keywords:
EfficientNetB0Invasive ductal carcinoma (IDC) and Non-IDC categoriesMatthews correlation coefficient (MCC)Mean absolute error (MAE)ResNet50

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Oncology

Background:

  • Breast cancer remains a leading cause of mortality, with diagnosis and treatment services impacted by global health crises like COVID-19.
  • The need for rapid, efficient, and accurate diagnostic tools for breast cancer is critical, yet current machine learning approaches face challenges in diagnostic accuracy.

Purpose of the Study:

  • To develop and evaluate a novel hybrid deep learning model for improved classification of breast histopathology images into invasive ductal carcinoma (IDC) and non-IDC categories.
  • To enhance diagnostic accuracy and efficiency in breast cancer screening through advanced computational methods.

Main Methods:

  • A hybrid model was developed by combining EfficientNetB0 and ResNet50 architectures.
  • Histopathology images were preprocessed, including resizing to 128*128 pixels and normalization, to optimize model performance.
  • The model leveraged EfficientNetB0's efficiency and ResNet50's deep residual connections to address vanishing gradients and improve classification.

Main Results:

  • The proposed model achieved a high accuracy of 94% in classifying breast histopathology images.
  • Performance metrics included a Mean Absolute Error (MAE) of 0.0628 and a Matthews Correlation Coefficient (MCC) of 0.8690.
  • The model demonstrated superior performance compared to previous baselines, balancing precision and recall effectively.

Conclusions:

  • The developed hybrid EfficientNetB0-ResNet50 model offers a resilient and accurate solution for breast cancer diagnosis.
  • This ensemble approach shows superiority in accuracy and computational efficiency, making it suitable for practical breast cancer screening and diagnosis.