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Updated: Sep 26, 2025

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Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset.

Adeyinka P Adedigba1, Steve A Adeshina2, Abiodun M Aibinu1

  • 1Department of Mechatronics Engineering, Federal University of Technology, Minna 920211, Nigeria.

Bioengineering (Basel, Switzerland)
|April 21, 2022
PubMed
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This study introduces a deep learning method for breast cancer (BC) diagnosis using digital mammograms. The approach achieved high accuracy, improving early detection rates and aiding in cancer treatment.

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Breast cancer (BC) is a leading global cancer, with rising incidence in low- and middle-income countries.
  • Early detection significantly improves treatment outcomes and reduces mortality by 25%.
  • Digital mammography is a key screening tool, but image analysis remains challenging.

Purpose of the Study:

  • To develop and evaluate a deep learning model for accurate breast cancer diagnosis from mammograms.
  • To enhance the efficiency and performance of deep learning models for medical image analysis.

Main Methods:

  • Implemented a discriminative fine-tuning method for deep convolutional neural networks (CNNs), dynamically adjusting learning rates per layer.
  • Utilized mixed-precision training to reduce computational load.
Keywords:
breast cancerdeep convolution neural networkdiscriminative fine-tuningmammogrammixed-precision training

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  • Applied data augmentation techniques specifically for mammogram images.

    • Main Results:

    • The discriminative fine-tuning approach facilitated rapid model convergence, achieving peak performance within 50 epochs.
    • DenseNet model achieved a high accuracy of 0.998.
    • AlexNet model demonstrated strong performance with an accuracy of 0.988.

    Conclusions:

    • The proposed deep learning method, featuring discriminative fine-tuning and mixed-precision training, significantly enhances breast cancer diagnosis accuracy from mammograms.
    • This approach offers a promising tool for improving early breast cancer detection and patient outcomes.