• DocumentCode
    2298520
  • Title

    Revised GMDH-type neural network using artificial intelligence and its application to medical image diagnosis

  • Author

    Kondo, Tadashi

  • Author_Institution
    Grad. Sch. of Health Sci., Univ. of Tokushima, Tokushima, Japan
  • fYear
    2011
  • fDate
    11-15 April 2011
  • Firstpage
    76
  • Lastpage
    83
  • Abstract
    A revised Group Method of Data Handling (GMDH)-type neural network algorithm using artificial intelligence technology for medical image diagnosis is proposed and is applied to medical image diagnosis of lung cancer. In this algorithm, the knowledge base for medical image diagnosis is used for organizing the neural network architecture for medical image diagnosis. Furthermore, the revised GMDH-type neural network algorithm has a feedback loop and can identify the characteristics of the medical images accurately using feedback loop calculations. The optimum neural network architecture fitting the complexity of the medical images is automatically organized so as to minimize the prediction error criterion defined as Prediction Sum of Squares (PSS). It is shown that the revised GMDH-type neural network is accurate and a useful method for the medical image diagnosis of lung cancer.
  • Keywords
    artificial intelligence; data handling; medical image processing; neural nets; artificial intelligence technology; feedback loop calculations; group method of data handling; lung cancer; medical image diagnosis; prediction sum of squares; revised GMDH-type neural network; Artificial neural networks; Cancer; Input variables; Lungs; Medical diagnostic imaging; Neurons; Artificial Intelligence; GMDH; Heuristic Self-Organization; Lung Cancer; Medical Image Diagnosis; Neural Networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Hybrid Intelligent Models And Applications (HIMA), 2011 IEEE Workshop On
  • Conference_Location
    Paris
  • Print_ISBN
    978-1-4244-9907-6
  • Type

    conf

  • DOI
    10.1109/HIMA.2011.5953960
  • Filename
    5953960