• DocumentCode
    2276245
  • Title

    Breast Cancer Detection Using Neural Network Models

  • Author

    Pawar, P.S. ; Patil, D.R.

  • Author_Institution
    Dept. of CE & IT, R.C. Patel Inst. of Technol., Shirpur, India
  • fYear
    2013
  • fDate
    6-8 April 2013
  • Firstpage
    568
  • Lastpage
    572
  • Abstract
    Breast cancer is the leading cause of death in women. If breast cancer is detected in early stage, then chances of survival are very high. In body new cells take place of old cells by orderly growth as old cells die out. The process of mutation controls the activation of genes in cells. Due to this cells get ability to go on dividing without control and producing cells like it, forming a tumor. This tumor can be of benign or malignant. The benign tumors are not dangerous while malignant tumors are dangerous to health. The unchecked malignant tumors have ability to spread in other parts of body. Breast cancer detection is complex process. So the computer-aided diagnosis of breast cancer helps physician in decision making. The system for breast cancer detection is developed using back propagation neural network and we compare its results with radial basis function network. After comparing we found back propagation neural network is the best technique to detect breast cancer.
  • Keywords
    backpropagation; biological organs; cancer; cellular biophysics; decision making; genetics; medical diagnostic computing; neural nets; patient diagnosis; tumours; backpropagation neural network model; benign tumor; breast cancer detection; cell; computer-aided diagnosis; decision making; gene; malignant tumor; Backpropagation; Biological neural networks; Breast cancer; Neurons; Testing; Training; backpropagation neural network; confusion matrix; radial basis function;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication Systems and Network Technologies (CSNT), 2013 International Conference on
  • Conference_Location
    Gwalior
  • Print_ISBN
    978-1-4673-5603-9
  • Type

    conf

  • DOI
    10.1109/CSNT.2013.122
  • Filename
    6524463