TY  - JOUR
TT  - Classification of Structural MRI for Detecting Alzheimer’s Disease
AU  - Demirhan, Ayşe
PY  - 2016
DA  - December
DO  - 10.18201/ijisae.270708
JF  - International Journal of Intelligent Systems and Applications in Engineering
PB  - İsmail SARITAŞ
WT  - DergiPark
SN  - 2147-6799
SP  - 195
EP  - 198
VL  - 4
IS  - Special Issue-1
KW  - Alzheimer’s Disease
KW  - neuroimaging
KW  - structural MRI
KW  - multivariate analysis
KW  - image classification
KW  - machine learning techniques
N2  - Alzheimer’s Disease (AD) is a pathological form of dementia that degeneratesbrain structures. AD affects millions of elderly people over the world and thenumber of people with AD doubles every year. Detecting AD years before theeffects of disease using structural magnetic resonance imaging (MRI) of thebrain is possible. Neuroimaging features that are extracted from the structuralbrain MRI can be used to predict AD by revealing disease related patterns.Machine learning techniques can detect AD and predict conversions from mildcognitive impairment (MCI) to AD automatically and successfully by using theseneuroimaging features. In this study common structural brain measures such asvolumes and thickness of anatomical structures that are obtained from The OpenAccess Series of Imaging Studies (OASIS) and made publicly available byhttps://www.nmr.mgh.harvard.edu/lab/mripredict are analysed. State-of-the-artmachine learning techniques, namely support vector machines (SVM), k-nearestneighbour (kNN) algorithm and backpropagation neural network (BP-NN) areemployed to discriminate AD and mild AD from healthy controls. Traininghyperparameters of the classifiers are tuned using classification accuracywhich is obtained with 5-fold cross validation. Prediction performance of thetechniques are compared using accuracy, sensitivity and specificity. Results ofthe system revealed that AD can be distinguished from the healthy controlssuccessfully using multivariate morphological features and machine learningtools. According to the performed experiments SVM is the most successfulclassifier for detecting AD with classification accuracies up to 82%.
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UR  - https://doi.org/10.18201/ijisae.270708
L1  - https://dergipark.org.tr/tr/download/article-file/237444
ER  -