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Edge detection algorithm of cancer image based on deep learning.

Xiaofeng Li1, Hongshuang Jiao2, Yanwei Wang3

  • 1Department of Information Engineering, Heilongjiang International University , Harbin, China.

Bioengineered
|June 23, 2020
PubMed
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This study introduces a deep learning edge detection algorithm for cancer images, improving 3D reconstruction accuracy to 95%. The new method enhances edge detection capabilities, providing smoother and more accurate results for cancer image analysis.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Oncology

Background:

  • Existing medical image edge detection algorithms suffer from low reconstruction accuracy and poor optimization, leading to reduced information recall, smoothness, and overall detection accuracy.
  • These limitations hinder effective cancer image analysis and diagnosis.

Purpose of the Study:

  • To propose a novel deep learning-based edge detection algorithm for cancer images.
  • To enhance the accuracy, smoothness, and information recall of cancer image edge detection.

Main Methods:

  • Constructed a three-dimensional (3D) surface structure reconstruction model for cancer images.
  • Employed an edge contour feature extraction method for fine-grained cancer cell feature identification.
  • Developed a multi-dimensional pixel feature distributed recombination model for regional fusion and information recombination, extracting ultra-fine particle features.
Keywords:
Deep learningcancer imagingedge detectionfeature extractionthree-dimensional reconstruction

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  • Integrated a deep learning algorithm for adaptive optimization of the edge detection process.

    • Main Results:

    • Achieved approximately 95% accuracy in 3D reconstruction.
    • Demonstrated high fitness of optimization coefficients.
    • Exhibited strong edge information detection ability with improved output result smoothness.
    • Showcased high accuracy in edge feature detection for cancer images.

    Conclusions:

    • The proposed deep learning algorithm significantly improves cancer image edge detection.
    • The method offers high accuracy, smoothness, and effective edge feature extraction, crucial for cancer diagnosis.