Computer based Classification of MR Scans in First Time Applicant Alzheimer Patients

Author(s): Fatma Polat, Selcuk Orhan Demirel, Omer Kitis, Fatma Simsek, Damla Isman Haznedaroglu, Kerry Coburn, Emre Kumral, Ali Saffet Gonul

Journal Name: Current Alzheimer Research

Volume 9 , Issue 7 , 2012

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In this study, we aimed to classify MR images for recognizing Alzheimer Disease (AD) in a group of patients who were recently diagnosed by clinical history and neuropsychiatric exams by using non-biased machine-learning techniques. T1 weighted MRI scans of 31 patients with probable AD and 31 age- and gender-matched cognitively normal elderly were analyzed with voxel-based morphometry and classified by support vector machine (SVM), a machine learning technique. SVM could differentiate patients from controls with accuracy of 74 % (sensitivity: 70 % and specificity: 77 %) when the whole brain was included the analyses. The classification accuracy was increased to 79 % (sensitivity: 65 % and specificity: 93 %) when the analyses restricted to hippocampus. Our results showed that SVM is a promising tool for diagnosis of AD, but needed to be improved.

Keywords: Alzheimer’s disease, classification, diagnoses, support vector machines, hippocampus, magnetic resonance imaging, hippocampus, cardiovascular disease.

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Article Details

Year: 2012
Page: [789 - 794]
Pages: 6
DOI: 10.2174/156720512802455359
Price: $65

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