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Related Experiment Video

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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A Two-Phase Deep Learning Approach for Architectural Distortion Detection in Mammograms.

Sameh E Ibrahim1, Mai S Mabrouk2, Wael A Mohamed3

  • 1Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, 13511, Benha, Egypt. sameh.metwaly@bhit.bu.edu.eg.

Journal of Imaging Informatics in Medicine
|July 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced deep learning model for detecting architectural distortion (AD) in mammograms. The AI system significantly improves early breast cancer detection accuracy and reduces radiologist workload.

Keywords:
Architectural distortion (AD)Breast cancerDeep learningEarly detectionMammographic imagesSegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of mortality in women globally, necessitating early detection for improved outcomes.
  • Architectural distortion (AD) is a subtle, early indicator of breast cancer on mammograms, challenging traditional detection methods.
  • Automating AD segmentation and classification is crucial to reduce radiologist workload and enhance diagnostic accuracy.

Purpose of the Study:

  • To develop and evaluate an automated deep learning approach for segmenting and classifying architectural distortion (AD) in mammograms.
  • To improve the accuracy and efficiency of early breast cancer detection by addressing the challenges of AD identification.
  • To reduce the burden on radiologists by providing a reliable AI-assisted diagnostic tool.

Main Methods:

  • A two-phase deep learning pipeline was developed, integrating U-Net++ for semantic segmentation and Mask R-CNN for instance segmentation.
  • A ResNet-18 classification model was combined with Mask R-CNN to refine AD predictions and minimize false positives.
  • Optimized loss functions, including smooth L1 and binary cross-entropy with Dice loss, were employed to enhance segmentation performance.

Main Results:

  • The integrated deep learning approach achieved high performance metrics: segmentation accuracy of 0.852, classification accuracy of 0.915, and mean average precision (mAP) of 0.894.
  • The system demonstrated a high sensitivity of 92.4% in detecting architectural distortion.
  • The proposed method significantly improved segmentation metrics and reduced false positives compared to traditional approaches.

Conclusions:

  • The developed deep learning pipeline offers a robust and accurate solution for automated AD segmentation and classification on mammograms.
  • This AI-driven approach has the potential to significantly enhance breast cancer screening and diagnostic processes.
  • Timely and accurate detection of AD through this technology can lead to improved patient outcomes via earlier treatment planning.