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Related Experiment Video

Updated: Aug 26, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Mammogram classification based on a novel convolutional neural network with efficient channel attention.

Qiong Lou1, Yingying Li1, Yaguan Qian1

  • 1School of Science, Zhejiang University of Science and Technology, Hangzhou 310012, China.

Computers in Biology and Medicine
|October 4, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-stage method for breast cancer mammogram analysis, improving diagnostic accuracy without manual region annotations. The approach enhances early detection and reduces mortality by optimizing computer-aided diagnosis (CAD) systems.

Keywords:
Efficient channel attentionFocal lossMammogram classificationResNet50Transfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Early and accurate mammography screening is crucial for reducing breast cancer mortality.
  • Current CNN-based CAD systems face challenges in precise lesion diagnosis due to low SNR and complex physiological characteristics.
  • Manual region of interest (ROI) annotations for training CAD systems are labor-intensive and resource-draining.

Purpose of the Study:

  • To develop an efficient, two-stage method for breast cancer mammogram diagnosis that overcomes the limitations of manual ROI annotation.
  • To improve the accuracy and reduce the mortality associated with breast cancer through enhanced computer-aided diagnosis (CAD).

Main Methods:

  • Proposed a novel Breast Database Preprocess (BDP) method for efficient mammogram data preparation, yielding the INbreast† dataset.
  • Implemented a two-stage approach combining image preprocessing with model optimization using focal loss on ECA-Net50 (an enhanced ResNet50 with efficient channel attention).
  • Utilized Grad-CAM for visualizing model attention to lesion regions.

Main Results:

  • Achieved an AUC of 0.960, accuracy of 0.929, recall of 0.928, and precision of 0.883 on the INbreast† dataset.
  • Demonstrated a significant precision improvement of 0.254 compared to standard ResNet50.
  • Visualizations confirmed the model's focus on relevant lesion areas, indicating effective feature extraction.

Conclusions:

  • The proposed method effectively preprocesses mammograms and optimizes CAD models, eliminating the need for manual ROI annotations.
  • The approach successfully addresses challenges of hard-to-classify samples and class imbalance, leading to superior diagnostic performance.
  • Both quantitative results and visual evidence support the method's satisfactory performance in mammographic lesion detection and diagnosis.