Document detail
ID
oai:arXiv.org:2007.00682
Topic
Electrical Engineering and Systems... Computer Science - Machine Learnin...Author
Mostafa, Tahjid Ashfaque Cheng, IreneCategory
Computer Science
Year
2020
listing date
3/31/2022
Keywords
architectures parkinsonAbstract
Parkinson's Disease(PD) is one of the major nervous system disorders that affect people over 60.
PD can cause cognitive impairments.
In this work, we explore various approaches to identify Parkinson's using Magnetic Resonance (MR) T1 images of the brain.
We experiment with ensemble architectures combining some winning Convolutional Neural Network models of ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and propose two architectures.
We find that detection accuracy increases drastically when we focus on the Gray Matter (GM) and White Matter (WM) regions from the MR images instead of using whole MR images.
We achieved an average accuracy of 94.7\% using smoothed GM and WM extracts and one of our proposed architectures.
We also perform occlusion analysis and determine which brain areas are relevant in the architecture decision making process.
Mostafa, Tahjid Ashfaque,Cheng, Irene, 2020, Parkinson's Disease Detection Using Ensemble Architecture from MR Images
A Novel MR Imaging Sequence of 3D-ZOOMit Real Inversion-Recovery Imaging Improves Endolymphatic Hydrops Detection in Patients with Ménière Disease
ménière disease p < detection imaging sequences 3d-zoomit 3d endolymphatic real tse reconstruction ir inversion-recovery hydrops ratio
