• DocumentCode
    2940589
  • Title

    Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification

  • Author

    Lau, Ho Tak ; Al-Jumaily, Adel

  • Author_Institution
    Sch. of Electr., Mech. & Mechatron. Syst., Univ. of Technol., Sydney, NSW, Australia
  • fYear
    2009
  • fDate
    4-7 Dec. 2009
  • Firstpage
    375
  • Lastpage
    380
  • Abstract
    In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo.
  • Keywords
    backpropagation; cancer; image classification; medical image processing; neural nets; object detection; skin; 3 layers backpropagation neural network classifier; automatically early skin cancer detection; automatically skin cancer classification system; dermoscopy photo; image database; image processing procedure; neural network classification; Cancer detection; Skin cancer; Skin cancer; classification; computer based detection; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Soft Computing and Pattern Recognition, 2009. SOCPAR '09. International Conference of
  • Conference_Location
    Malacca
  • Print_ISBN
    978-1-4244-5330-6
  • Electronic_ISBN
    978-0-7695-3879-2
  • Type

    conf

  • DOI
    10.1109/SoCPaR.2009.80
  • Filename
    5370977