Title: Computer aided detection of imaging biomarkers for Alzheimer's disease
Authors: G. Sateesh Babu; B.S. Mahanand
Addresses: School of Computer Science and Engineering, Nanyang Technological University, Singapore ' Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, India
Abstract: In this paper, we present a novel approach for the computer aided detection of imaging biomarkers responsible for Alzheimer's disease (AD) from magnetic resonance imaging (MRI) using meta-cognitive radial basis function network (McRBFN) classifier. The McRBFN classifier uses voxel-based morphometric features extracted from MRI and employs a sequential projection-based learning (PBL) algorithm for classification. We propose a recursive feature elimination approach (called PBL-McRBFN-RFE) to identify the most relevant and meaningful imaging biomarkers for AD detection. The study has been conducted using the well-known open access series of imaging studies dataset. The brain regions identified by the PBL-McRBFNRFE feature selection approach include hippocampus, parahippocampal gyrus, superior temporal gyrus, insula, precentral gyrus and extra nuclear, which have also been reported as critical regions in the medical literature. Further, we also conducted a study based on the age to identify the brain regions responsible for the onset of AD.
Keywords: magnetic resonance imaging; MRI; radial basis function network classifier; voxel-based morphometry; VBM; meta-cognitive learning algorithm; Alzheimer's disease; recursive feature elimination; RFE.
International Journal of Signal and Imaging Systems Engineering, 2021 Vol.12 No.3, pp.108 - 118
Accepted: 16 Aug 2017
Published online: 04 Oct 2021 *