Open AccessAutomatic Early Detection of Alzheimer’s D isease b ased on 2D VMD and Deep Convolutional Neural N etwork
Author(s) -
Bhanja Kishor Swain,
Susanta Kumar Rout,
Rajendra Sharma
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a2134.109119
Subject(s) - convolutional neural network , artificial intelligence , pattern recognition (psychology) , deep learning , computer science , cognition , cognitive impairment , magnetic resonance imaging , psychology , neuroscience , medicine , radiology
In this paper, the classification of normal controls (NC), very mild cognitive impairment and the early stage of Alzheimer’s disease (AD) known as mild cognitive impairment (MCI) from magnetic resonance imaging (MRI) is proposed, based on the two dimensional variational mode decomposition (2D-VMD) and deep convolutional neural network (DCNN). The 2D-VMD is applied to decompose the MRI scans into a discrete number of band limited intrinsic mode functions (BLIMFs). The automatic feature extraction, selection and optimization are performed using the proposed DCNN. The classification accuracy and learning speed of the 2D-VMD-DCNN method are compared with DCNN by taking the MRI data as input. The superior classification accuracy of the proposed 2D-VMD-DCNN method over DCNN method as well as other recently introduced prevalent methods is the major advantage for analyzing the biomedical images in the field of health care.