• DocumentCode
    3073876
  • Title

    Neural networks for the detection and localization of breast cancer

  • Author

    Abbosh, Younis M. ; Yahya, Ammar F. ; Abbosh, Amin

  • Author_Institution
    Coll. of Electron. Eng., Mosul Univ., Mosul, Iraq
  • fYear
    2011
  • fDate
    29-31 March 2011
  • Firstpage
    156
  • Lastpage
    159
  • Abstract
    This paper investigates the use of neural networks to detect and locate early breast cancer using a simple feed-forward back-propagation neural network. In order to test the proposed algorithm, an electromagnetic simulator is used to build a three-dimensional breast model. Spherical tumors of radii 1 mm, 2 mm, 4 mm, and 5 mm are assumed to be at different locations in the breast model. An ultra-wideband pulse is transmitted towards the breast model and four probes are located around the breast to capture the scattered signals. The collected signals are then analyzed using the neural networks to get useful information concerning the presence or otherwise of the tumor and its location if it does exist. The obtained results from using the proposed method are promising with 100% success in the detection and 95% success in the localization.
  • Keywords
    backpropagation; biological organs; biomedical imaging; cancer; feedforward neural nets; mammography; medical diagnostic computing; microwave imaging; physiological models; tumours; ultra wideband technology; breast cancer; electromagnetic simulator; feed-forward back-propagation neural network; radius 1 mm; radius 2 mm; radius 4 mm; radius 5 mm; three-dimensional breast model; tumors; ultra-wideband pulse; Artificial neural networks; Data models; Electromagnetics; Permittivity; Radar imaging; Skin; Tumors; breast cancer recognition; medical imaging; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications and Information Technology (ICCIT), 2011 International Conference on
  • Conference_Location
    Aqaba
  • Print_ISBN
    978-1-4577-0401-7
  • Type

    conf

  • DOI
    10.1109/ICCITECHNOL.2011.5762669
  • Filename
    5762669