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Computer based Classification of MR Scans in First Time Applicant Alzheimer Patients
(BENTHAM SCIENCE PUBL LTD, 2012-08-01) Fatma Polat; Selcuk Orhan Demirel; Omer Kitis; Fatma Simsek; Damla Isman Haznedaroglu; Kerry Coburn; Emre Kumral; Ali Saffet Gonul
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.