Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers

Year 2016, Volume: 4 Issue: Special Issue-1, 170 - 174, 25.12.2016

Nihat Adar , Savaş Okyay Kemal Özkan , Suzan Şaylısoy , Belgin Demet Özbabalık Adapınar , Baki Adapınar

https://doi.org/10.18201/ijisae.270422

Cited By: 1

Abstract

Dementias are termed as neuropsychiatric disorders. Brain images of
dementia patients can be obtained through magnetic resonance imaging systems.
The relevant disease can be diagnosed by examining critical regions of those
images. Certain brain characteristics such as the cortical volume, the
thickness, and the surface area may vary among dementia types. These attributes
can be expressed as numerical values using image processing techniques. In this
study, the dataset involves T1 medical image sets of 63 samples. Each
particular sample is labeled with one of the three dementia types: Alzheimer's
disease, frontotemporal dementia, and vascular dementia. The image sets are
processed to create different feature groups. These are cortical volumes, gray
volumes, surface areas, and thickness averages. The main objective is seeking
brain sections more effective in establishing the clinical diagnosis. In other
words, searching an optimal feature subset process is carried out for each
feature group. To that end, a wrapper feature selection technique namely
genetic algorithm is used with Naive Bayes classifier and support vector
machines. The test phase is performed by using 10-fold cross validation. Consequently, accuracy results up to 93.7% with
different classifiers and feature selection parameters are shown.

Keywords

Genetic algorithm, Feature selection, Dementia, Classification, Magnetic resonance imaging

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