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Brain pattern recognition based classification of neurodegenerative diseases

机译:基于脑模式识别的神经退行性疾病分类

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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.
机译:通过医学成像对神经退​​行性疾病进行视觉检查是一项繁琐且容易出错的任务。放射科医生多数时候会误认为该疾病是正常的衰老效应。在该项目中,引入了一种系统,该系统可以自动对神经退行性疾病的类型进行分类。在输入的磁共振图像(MRI)中已经完成了基本图像处理,例如预处理和特征提取。神经网络方法已用于测试和训练图像,之后是灰度协矩阵(GLCM)。这些特征经过神经网络的训练阶段,然后进行测试以对神经退行性疾病的种类进行分类,无论是帕金森氏症还是精神分裂症或正常人。支持向量机(SVM)是此处选择的神经网络。

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