Evaluation of Feature Selection Algorithms for Detection of Depression from Brain sMRI Scans

摘要：

　　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.

关键词： structural MRI brain image analysis feature selection depression detection

作者: Kuryati Kipli Abbas Z. Kouzani Matthew Joordens

作者单位: School of Engineering Deakin University Geelong, Victoria 3216, Australia

会议类型: 国际会议

会议名称: 2013 ICME International Conference on Complex Medical Engineering(2013 ICME复合医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 64-69

在线出版日期: 2013-05-25（万方平台首次上网日期，不代表论文的发表时间）