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

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

Volume: 9 Issue: 7

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

Affiliation:

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

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.

Cite this article as:

Polat Fatma, Orhan Demirel Selcuk, Kitis Omer, Simsek Fatma, Isman Haznedaroglu Damla, Coburn Kerry, Kumral Emre and Saffet Gonul Ali, Computer based Classification of MR Scans in First Time Applicant Alzheimer Patients, Current Alzheimer Research 2012; 9 (7) . https://dx.doi.org/10.2174/156720512802455359