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All Journal International Journal of Intelligent Systems and Applications in Engineering
Adapınar, Belgin Demet Özbabalık
Prof. Dr. Ismail SARITAS
Computer Science & IT

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Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers Adar, Nihat; Okyay, Savaş; Özkan, Kemal; Şaylısoy, Suzan; Adapınar, Belgin Demet Özbabalık; Adapınar, Baki
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.270422

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: Alzheimers 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.Anahtar Kelimeler
Co-Authors Adapınar, Baki Adar, Nihat Özkan, Kemal Şaylısoy, Suzan Okyay, Savaş