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Extracting 2D Shape Features for Early Detection of Alzheimers disease


Mrs.K.Emily Esther Rani , Jayaraj Annapackiam CSI college of Engineering,Nazareth; Dr.S.Baulkani, Government College of Engineering, Srirangam Tamil Nadu, ; M.Divya Nanthini, Jayaraj Annapackiam CSI college of Engineering,Nazareth; P.Keerthana, Jayaraj Annapackiam CSI college of Engineering,Nazareth; R.Kavitha, Jayaraj Annapackiam CSI college of Engineering,Nazareth


Alzheimer’s disease, Dementia, Magnetic Resonance Imaging(MRI), Mild Cognitive Impairment (MCI), Support Vector Machine(SVM)


AD is a progressive disease which in its early stages, memory loss is mild, but in severe stage, patients lose the ability to speak and respond to their environment as a result of the brain tissue degeneration The diagnosis of Alzheimer Disease (AD) in its earlier stage is very important. Because early diagnosis is used to prevent a patient from death. The proposed system helps to diagnosis accurate detection of Alzheimer’s disease. In this paper, the input Magnetic Resonance Image (MRI) is preprocessed using median filters and then segmented into Grey Matter (GM), White Matter (WM) and Ventricle. The useful and informative features like shape features are extracted from segmented MRI images for early detection of AD. Finally, a linear Support Vector machine (SVM) is implemented to differentiate AD subjects with normal subjects. Experimental results are shown to demonstrate the accuracy of the proposed method.

Other Details

Paper ID: ICCTP006
Published in: Conference 11 : ICCT 19
Publication Date: 01/05/2019
Page(s): 30-34

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