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Fully Automated Breast Density Segmentation and Classification Using Deep Learning.

Nasibeh Saffari1, Hatem A Rashwan1, Mohamed Abdel-Nasser1,2

  • 1Intelligent Robotics and Computer Vision Group, Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, 43007 Tarragona, Spain.

Diagnostics (Basel, Switzerland)
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PubMed
Summary
This summary is machine-generated.

This study introduces a fully automated deep learning system for breast density classification. Advanced techniques accurately segment dense breast tissue, improving diagnostic tools for mammography.

Keywords:
breast cancerbreast densityconvolutional neural networkdeep learninggenerative adversarial networksmammograms

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Biomedical Image Analysis

Background:

  • Visual breast density estimation from mammograms is challenging due to low contrast and fatty tissue variability.
  • Existing automated methods are often affected by noise and texture variations, limiting their clinical utility.
  • Accurate detection of dense breast tissue is crucial for reliable breast density classification.

Purpose of the Study:

  • To develop a fully automated and digitalized system for breast tissue segmentation and classification.
  • To enhance the accuracy and reliability of breast density analysis in mammography using deep learning.
  • To create a clinically useful computer-aided tool for breast density assessment.

Main Methods:

  • Utilized conditional Generative Adversarial Networks (cGAN) for segmenting dense breast tissues in mammograms.
  • Employed a Convolutional Neural Network (CNN) for classifying mammograms based on BI-RADS standards, using cGAN-generated segmentation masks.
  • The framework was evaluated on 410 screening mammograms from 115 patients in the INbreast dataset.

Main Results:

  • The cGAN model achieved high performance in dense region segmentation, with accuracy, Dice coefficient, and Jaccard index of 98%, 88%, and 78%, respectively.
  • The CNN classification network demonstrated excellent diagnostic capabilities, yielding precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%.
  • The integrated deep learning framework provides robust performance for breast density analysis.

Conclusions:

  • The proposed deep learning framework offers a promising, fully automated solution for breast density segmentation and classification.
  • The advanced techniques, including cGAN and CNN, significantly improve the accuracy and reliability of breast density assessment.
  • This system has the potential to become a valuable computer-aided diagnostic tool in digital mammography screening programs.