Multiple Sclerosis Slice Identification by Haar Wavelet Transform and Logistic Regression

DOI

10.2991/ammee-17.2017.10How to use a DOI?

Keywords

multiple sclerosis; slice identification; Haar wavelet transform; logistic regression

Abstract

(Aim) Currently, scholars tend to use computer vision approaches to implement multiple sclerosis (MS) identification. (Method) In this study, we proposed a novel MS slice identification system, based on Haar wavelet transform, principal component analysis, and logistic regression. (Result) Simulation results showed the accuracies of our method using 2-level, 3-level, and 4-level decomposition are 83.25ñ1.62%, 89.72ñ1.18%, and 87.65ñ1.79%, respectively. (Conclusion) Our method with 3-level decomposition achieved the best.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Xueyan Wu
AU  - Mason Lopez
PY  - 2017/06
DA  - 2017/06
TI  - Multiple Sclerosis Slice Identification by Haar Wavelet Transform and Logistic Regression
BT  - Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SP  - 50
EP  - 55
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.10
DO  - 10.2991/ammee-17.2017.10
ID  - Wu2017/06
ER  -