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Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images.

Mohammad Alkhaleefah1, Tan-Hsu Tan1, Chuan-Hsun Chang2

  • 1Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Cancers
|August 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces Connected-SegNets, a novel deep learning model for enhanced breast tumor segmentation in X-ray images. The model achieves superior performance using intersection over union loss and advanced data augmentation techniques.

Keywords:
X-ray imagesbreast tumor segmentationconvolutional neural networkdeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate breast tumor segmentation is crucial for effective diagnosis and treatment planning in mammography.
  • Existing deep learning models face challenges with noise and data variability in medical images.

Purpose of the Study:

  • To propose Connected-SegNets, a novel deep learning architecture for improved breast tumor segmentation in X-ray images.
  • To enhance model robustness and accuracy through architectural modifications and optimized loss functions.

Main Methods:

  • Developed Connected-SegNets by integrating two SegNet architectures with skip connections.
  • Implemented intersection over union (IoU) loss to improve noise robustness.
  • Applied contrast limit adaptive histogram equalization (CLAHE) for preprocessing and rotation/flipping for data augmentation.

Main Results:

  • Connected-SegNets outperformed state-of-the-art methods on INbreast, CBIS-DDSM, and a private dataset.
  • Achieved a maximum Dice score of 96.34% on INbreast and 92.86% on CBIS-DDSM.
  • Attained the highest IoU scores, reaching 91.21% on INbreast and 87.34% on CBIS-DDSM.

Conclusions:

  • Connected-SegNets demonstrate significant potential for accurate and robust breast tumor segmentation.
  • The proposed model offers a promising advancement in AI-driven medical image analysis for breast cancer detection.