Computer based classification of MR scans in first time applicant Alzheimer patients.
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.
1Beysehir State Hospital, Konya Turkey.
Fatma
F
Polat
Selcuk Orhan
SO
Demirel
Omer
O
Kitis
Fatma
F
Simsek
Damla Isman
DI
Haznedaroglu
Kerry
K
Coburn
Emre
E
Kumral
Ali Saffet
AS
Gonul
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000473:pathology; D001921:Brain / Q000473:pathology*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D012680:Sensitivity and Specificity; D060388:Support Vector Machine