Analysis of Alzheimer Condition in T1-Weighted MR Images Using Texture Features and K-NN Classifier

(AD) Alzheimer's disease is widely recognized as a disorder of the mental processes initiated by brain disease which a□ects more than 35 million individuals around the world. Structural Magnetic Resonance (MR) imaging strategies noninvasive tool and it assumes a significant part in analyzing Structural brain changes in subjects with neurodegenerative infections. Magnetic resonance imaging which delineates the pathology of brain structures and enhances the finding of the AD. This paper gives an analysis of Alzheimer Condition in Tl-Weighted MR Images using Texture Features and k-NN Classifier. Texture features are extracted utilizing GLCM to identify the AD and the Normal Control (NC). With a specific end goal to assess the proposed technique, performed assessments on the MRI securing from the OASIS database. The proposed strategy yields a normal testing exactness of 74.73% which demonstrates that the proposed technique can separate AD and NC agreeably.