• DocumentCode
    2626052
  • Title

    The Segmentation of Skin Cancer Image Based on Genetic Neural Network

  • Author

    Jianli, Liu ; Baoqi, Zuo

  • Author_Institution
    Soochow Univ., Suzhou, China
  • Volume
    5
  • fYear
    2009
  • fDate
    March 31 2009-April 2 2009
  • Firstpage
    594
  • Lastpage
    599
  • Abstract
    The segmentation of medical images is an important component of medical imaging technology, and the effect of which will impact the diagnosis and therapy directly. Taking the complexity and uncertainty of the medical images into consideration fully, we propose the genetic neural network to be used to segment the skin cancer images. Optimization of weights and thresholds in neural network based on genetic algorithm is executed to improve the convergence speed of the BP neural network. Compared with the standard BP neural network, the segmentation speed of the genetic neural network adopted in this paper is much higher. The skin cancer images segmented by this method have continuous edge and clear contour, which can be used in the quantitative analysis and identification of the skin cancer.
  • Keywords
    backpropagation; cancer; genetic algorithms; image segmentation; medical image processing; neural nets; radiation therapy; skin; BP neural network; genetic neural network; medical image segmentation; optimization; quantitative analysis; skin cancer diagnosis; theraphy; Biomedical imaging; Convergence; Genetic algorithms; Image analysis; Image segmentation; Medical diagnostic imaging; Medical treatment; Neural networks; Skin cancer; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Engineering, 2009 WRI World Congress on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-0-7695-3507-4
  • Type

    conf

  • DOI
    10.1109/CSIE.2009.53
  • Filename
    5170604