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