Computer based classification of MR scans in first time applicant Alzheimer patients.

Abstract:

: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.

journal_name

Curr Alzheimer Res

authors

Polat F,Demirel SO,Kitis O,Simsek F,Haznedaroglu DI,Coburn K,Kumral E,Gonul AS

doi

10.2174/156720512802455359

subject

Has Abstract

pub_date

2012-09-01 00:00:00

pages

789-94

issue

7

eissn

1567-2050

issn

1875-5828

pii

CAR-EPUB-20120130-006

journal_volume

9

pub_type

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