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Related Experiment Video

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Enhancing Breast Density Assessment in Mammograms Through Artificial Intelligence.

Naila Camila da Rocha1,2, Abner Macola Pacheco Barbosa3,4, Yaron Oliveira Schnr4

  • 1University of Wisconsin-Madison, 1675 Observatory Dr, Madison, WI, 53706, USA. ndarocha@wisc.edu.

Journal of Imaging Informatics in Medicine
|September 5, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an accessible, open-source AI tool for consistent breast density classification in mammograms, improving early breast cancer detection. The AI model demonstrates high accuracy, aiding radiologists in objective breast density assessment.

Keywords:
Breast cancerBreast densityComputer-aided diagnosisConvolutional neural networksMammography

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Computer Vision for Healthcare
  • Radiology and Diagnostic Imaging

Background:

  • Breast cancer remains a leading cause of cancer deaths in women globally.
  • Mammography is crucial for early detection, with breast density being a key factor.
  • Current breast density assessments (BI-RADS) are subjective and vary, necessitating objective tools.

Purpose of the Study:

  • To develop and validate an open-source, AI-driven computer vision approach for objective breast density classification.
  • To create an accessible, low-cost solution for consistent breast density assessment, especially in resource-limited settings.
  • To enhance early breast cancer detection through improved mammogram interpretation.

Main Methods:

  • A custom-designed convolutional neural network (CD-CNN) integrated with an extreme learning machine (ELM) layer was developed.
  • The model was trained and tested on a retrospective dataset of 10,371 full-field digital mammography images categorized by BI-RADS density (A-D).
  • Performance was evaluated using accuracy, sensitivity, specificity, and weighted kappa, including validation on the mini-MIAS dataset.

Main Results:

  • The proposed AI model achieved high performance on the primary dataset: 95.4% accuracy, 98.0% specificity, and 92.5% sensitivity.
  • Strong agreement was observed between the automated classification and expert consensus (weighted kappa = 0.90).
  • Comparable results were obtained on the independent mini-MIAS dataset, demonstrating robustness and generalizability.

Conclusions:

  • The developed open-source AI approach offers a consistent and accurate method for breast density assessment in mammograms.
  • This tool has the potential to significantly support early breast cancer detection efforts, particularly in underserved healthcare environments.
  • The integration of CD-CNN and ELM advances automated mammographic analysis for improved diagnostic consistency.