To facilitate fast diagnosis and to get accurate result of complex diseases in the individual patients it is necessary to have some automatic classification of diseases. The main objective of this paper is to utilize AI for diagnosis. This paper proposes to take into account the changes in white matter and the gray matter of the brain and its association with global disease severity in patients. The volumes were processed to perform a voxel based analysis of gray matter and white matter volumes. The gray matter  and white matter  volumes and their correlation with cognitive functions was investigated using  SVM classifier  .The support vector machine is a increasingly popular tool for machine learning tasks such as classification. SVM, a powerful machine method has been developed from statistical learning and has significant achievement in some field. The foundations of SVM have been developed by Vapnik and are gaining popularity in field of machine learning due to many attractive features and promising empirical performance. Introduced in the early 90’s, they led to an explosion of interest in machine learning. SVM method does not suffer the limitations of data dimensionality and limited samples [13]. The proposed method showed high accuracy and therefore may help in early diagnosis of diseases.

Keywords: Gray matter; white matter; SVM classifier; CT; MRI; neurodegenerative diseases