Evaluation of feature selection algorithms for detection of depression from brain sMRI scans

Kipli, Kuryati, Kouzani, Abbas Z. and Joordens, Matthew 2013, Evaluation of feature selection algorithms for detection of depression from brain sMRI scans, in ICME 2013 : Proceedings of the International Conference on Complex Medical Engineering (CME), IEEE, Piscataway, N.J., pp. 64-69.

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Title Evaluation of feature selection algorithms for detection of depression from brain sMRI scans
Author(s) Kipli, Kuryati
Kouzani, Abbas Z.
Joordens, Matthew
Conference name Complex Medical Engineering. International Conference (2013 : Beijing, China)
Conference location Beijing, China
Conference dates 25-28 May. 2013
Title of proceedings ICME 2013 : Proceedings of the International Conference on Complex Medical Engineering (CME)
Editor(s) [Unknown]
Publication date 2013
Conference series International Conference on Complex Medical Engineering
Start page 64
End page 69
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) brain image analysis
depression detection
feature selection
structural MRI
Summary Detection of depression from structural MRI (sMRI) scans is relatively new in the mental health diagnosis. Such detection requires processes including image acquisition and pre-processing, feature extraction and selection, and classification. Identification of a suitable feature selection (FS) algorithm will facilitate the enhancement of the detection accuracy by selection of important features. In the field of depression study, there are very limited works that evaluate feature selection algorithms for sMRI data. This paper investigates the performance of four algorithms for FS of volumetric attributes in sMRI scans. The algorithms are One Rule (OneR), Support Vector Machine (SVM), Information Gain (IG) and ReliefF. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. The result of the evaluation of the FS algorithms is discussed by using a number of analyses.
ISBN 9781467329712
9781467329705
Language eng
Field of Research 090304 Medical Devices
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057167

Document type: Conference Paper
Collection: School of Engineering
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