• DocumentCode
    2033079
  • Title

    Classification method for degree of lung adenocarcinoma differentiation

  • Author

    Murakami, Naoki ; Kanako, Toshiyuki ; Tanaka, Toshiyuki

  • Author_Institution
    Sch. of Fundamental Sci. & Technol., Keio Univ., Yokohama, Japan
  • fYear
    2011
  • fDate
    13-18 Sept. 2011
  • Firstpage
    1501
  • Lastpage
    1504
  • Abstract
    The number of fatalities from lung cancer accounts for 17% of that from all cancer, and is the highest ratio. Of them, the ratio of adenocarcinoma which has the highest ratio of lung cancer is increasing yearly. On the other hands, a classification of degree of differentiation is important to estimate prognosis, to determine the most suitable remedy and to investigate the relationship between smokers and patients of adenocarcinoma. Then we proposed new method for automatically classifying degree of adenocarcinoma differentiation. In this paper, we show the effectiveness of our method with results of classification.
  • Keywords
    cancer; differentiation; image classification; lung; medical image processing; neural nets; lung adenocarcinoma differentiation degree classification; lung cancer; neural network topology; Accuracy; Biological neural networks; Cancer; Cavity resonators; Correlation; Feature extraction; Lungs; case classification; image processing; lung cancer; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SICE Annual Conference (SICE), 2011 Proceedings of
  • Conference_Location
    Tokyo
  • ISSN
    pending
  • Print_ISBN
    978-1-4577-0714-8
  • Type

    conf

  • Filename
    6060199