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Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer

Cheng-Jian Lin1,2, Shiou-Yun Jeng1

  • 1Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.

Diagnostics (Basel, Switzerland)
|September 5, 2020
PubMed
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This study optimized convolutional neural network (CNN) parameters for breast cancer diagnosis using histopathological images. The uniform experimental design approach achieved 84.41% classification accuracy, improving upon existing methods.

Area of Science:

  • Medical image analysis
  • Computational pathology
  • Artificial intelligence in oncology

Background:

  • Breast cancer diagnosis relies heavily on histopathological image analysis.
  • Convolutional neural networks (CNNs) show promise for automated breast cancer classification from histopathology.
  • Challenges include complex CNN parameter tuning and time-intensive data processing.

Purpose of the Study:

  • To simplify and enhance CNN-based breast cancer classification from histopathological images.
  • To optimize CNN parameters using a uniform experimental design (UED).
  • To improve classification accuracy and efficiency in breast cancer diagnosis.

Main Methods:

  • Implementation of a uniform experimental design (UED) for CNN parameter optimization.
Keywords:
breast cancerconvolutional neural networkdeep learninghistopathologyuniform experimental design

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  • Application of regression analysis within UED to fine-tune model parameters.
  • Classification of breast cancer histopathological images using the optimized CNN model.

    • Main Results:

    • The proposed method achieved a classification accuracy rate of 84.41%.
    • UED-based parameter optimization effectively improved classification performance.
    • The results demonstrated superior performance compared to similar existing methods.

    Conclusions:

    • The UED approach offers an effective strategy for optimizing CNN parameters in medical image analysis.
    • This method enhances classification accuracy for breast cancer diagnosis.
    • The findings suggest a more efficient and accurate approach to computer-aided breast cancer detection.