Document Detail


Computer Based Classification of MR Scans in First Time Applicant Alzheimer Patients.
MedLine Citation:
PMID:  22299620     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
F Fatma Polat; S O Demirel; O Kitis; F Simsek; D I Haznedaroglu; K Coburn; E Kumral; A S Gonul
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-1-30
Journal Detail:
Title:  Current Alzheimer research     Volume:  -     ISSN:  1875-5828     ISO Abbreviation:  -     Publication Date:  2012 Jan 
Date Detail:
Created Date:  2012-2-3     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101208441     Medline TA:  Curr Alzheimer Res     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Ege University School of Medicine Department of Psychiatry SoCAT Lab, Bornova, 35100, Izmir, Turkey. simsek.fatma@gmail.com.
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