• DocumentCode
    1580406
  • Title

    Brain pattern recognition based classification of neurodegenerative diseases

  • Author

    Happila, T. ; Kingston, Stanley P.

  • Author_Institution
    Dept. of Electron. & Instrum. Eng., Karunya Univ., Coimbatore, India
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    The visual inspection of the neurodegenerative disease through medical imaging is a tedious and error prone task. Most of the times the radiologist can misunderstand the disorder to be normal aging effect. In this project a system is being introduced which automatically classifies the kind of neurodegenerative disease. Basic image processing like preprocessing followed by feature extraction have been done in input Magnetic Resonance Image (MRI). Neural network methodologies have been used for testing and training the image which is preceded by Gray Level Co Matrix (GLCM). These features undergo a training phase of neural network followed by testing to classify the kind of neurodegenerative disease whether Parkinson or Schizophrenia or Normal. Support Vector Machine (SVM) is the neural network which is opted here.
  • Keywords
    biomedical MRI; brain; diseases; feature extraction; image classification; medical image processing; neural nets; Gray Level Co Matrix; Parkinson disease; Schizophrenia; brain pattern recognition; feature extraction; magnetic resonance image; medical imaging; neural network methodology; neurodegenerative disease classification; normal aging effect; support vector machine; visual inspection; Diseases; Energy measurement; Feature extraction; Indexes; Medical diagnostic imaging; Support vector machines; Gray Level Co Matrix (GLCM); Neurodegenerative disease; Support Vector Machine (SVM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on
  • Conference_Location
    Coimbatore
  • Print_ISBN
    978-1-4799-6817-6
  • Type

    conf

  • DOI
    10.1109/ICIIECS.2015.7193135
  • Filename
    7193135