• DocumentCode
    2466300
  • Title

    Extension Neural Network Approach to Classification of Brain MRI

  • Author

    Wang, Chuin-Mu ; Wu, Ming-Ju ; Chen, Jian-Hong ; Yu, Cheng-Yi

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Chin-Yi Inst. of Technol., Taichung, Taiwan
  • fYear
    2009
  • fDate
    12-14 Sept. 2009
  • Firstpage
    515
  • Lastpage
    517
  • Abstract
    Magnetic resonance image (MRI) has been widely used for clinical applications in recent years. With the ability of scanning the same section by multiple frequencies, MRI makes it possible to generate several images on the same section. Despite of accessible abundant information, MRI also makes it more difficult to judge the location of every tissue. MRI will complicate the judgment due to strong noise. In order to resolve this problem, this paper endeavors to classify them via the help of extension neural network (ENN), This paper has to demonstrate the advantages of extension theory, statistical theory is considered as a judgment method, whereby obtaining experimental data of extension neural network and perceptron for subsequent comparison. It has proved that extension is superior to the other algorithms in terms of classification.
  • Keywords
    brain; image classification; magnetic resonance imaging; medical image processing; neural nets; patient treatment; ENN; brain MRI; clinical application; extension neural network approach; extension theory; image classification; magnetic resonance image; patient diagnosis; patient treatment; Artificial neural networks; Biological neural networks; Biomedical imaging; Diseases; Instruments; Magnetic resonance imaging; Medical diagnostic imaging; Pathology; Training data; X-ray imaging; Classification; Extension; MRI; Neural Network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on
  • Conference_Location
    Kyoto
  • Print_ISBN
    978-1-4244-4717-6
  • Electronic_ISBN
    978-0-7695-3762-7
  • Type

    conf

  • DOI
    10.1109/IIH-MSP.2009.141
  • Filename
    5337564