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Dense pooling layers in fully convolutional network for skin lesion segmentation.

Ebrahim Nasr-Esfahani1, Shima Rafiei1, Mohammad H Jafari2

  • 1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|October 22, 2019
PubMed
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This summary is machine-generated.

A novel deep learning network with dense pooling layers improves skin lesion segmentation accuracy. This method enhances border detection, outperforming current algorithms for computerized skin cancer detection.

Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Dermatology

Background:

  • Accurate lesion segmentation and border detection are crucial for computerized skin cancer detection.
  • Existing segmentation methods often struggle with precise border delineation in skin images.

Purpose of the Study:

  • To propose a new fully convolutional network architecture for improved skin lesion segmentation.
  • To enhance the accuracy of border detection in medical image analysis, specifically for skin lesions.

Main Methods:

  • Development of a novel fully convolutional network incorporating new dense pooling layers.
  • Application of the proposed network to skin lesion datasets for segmentation tasks.

Main Results:

Keywords:
Deep neural networksDense pooling layerMelanomaSkin cancer

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  • The proposed network achieves highly accurate segmentation of skin lesions.
  • The method demonstrates superior performance compared to state-of-the-art algorithms in skin lesion segmentation tasks.

    • Conclusions:

    • The novel network with dense pooling layers offers a significant advancement in skin lesion segmentation.
    • This approach holds promise for improving the accuracy of automated skin cancer detection systems.