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Skin Cancer01:30

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GA-Net: Ghost convolution adaptive fusion skin lesion segmentation network.

Longsong Zhou1, Liming Liang2, Xiaoqi Sheng3

  • 1School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, 341000, China; Jinguan Copper Branch of Tongling Nonferrous Metals Group Co, Ltd, Tongling, Anhui, 244100, China.

Computers in Biology and Medicine
|August 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel ghost convolution adaptive fusion network for improved skin lesion segmentation in medical imaging. The new method enhances early skin cancer detection by accurately identifying lesion details.

Keywords:
Adaptive fusion moduleBilateral attention moduleGhost convolutionLayer feature fusionSkin lesion segmentation

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Area of Science:

  • Computer-aided diagnosis
  • Medical image analysis
  • Dermatology

Background:

  • Accurate skin lesion segmentation is crucial for early skin cancer detection and treatment.
  • Existing deep learning methods often struggle with extracting detailed lesion features, leading to incomplete segmentation.
  • Challenges include missing information and inaccurate segmentation boundaries in skin lesion images.

Purpose of the Study:

  • To propose a novel ghost convolution adaptive fusion network for enhanced skin lesion segmentation.
  • To improve the extraction of detailed lesion features and segmentation accuracy.
  • To provide a more effective tool for computer-aided diagnosis of skin diseases.

Main Methods:

  • Incorporation of ghost modules for comprehensive feature extraction.
  • Utilizing adaptive fusion and bilateral attention modules to integrate shallow and deep network information.
  • Employing multi-level output patterns and layer feature fusion for improved pixel prediction and segmentation accuracy.

Main Results:

  • The proposed network achieved high accuracies (96.42% on ISIC2016, 94.07% on ISIC2017, 95.03% on ISIC2018) and kappa coefficients.
  • Demonstrated superior performance compared to existing networks on public datasets.
  • Showcased enhanced segmentation results for skin lesion images in simulation experiments.

Conclusions:

  • The ghost convolution adaptive fusion network significantly improves skin lesion segmentation accuracy.
  • The method offers a promising advancement for computer-aided diagnosis and early detection of skin cancer.
  • This approach presents new possibilities for the accurate diagnosis and management of skin diseases.