Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)

Abnormal Breast Detection Via Combination of Particle Swarm Optimization and Biogeography-Based Optimization

Authors
Fangyuan Liu, Koji Nakamura, Rodney Payne
Corresponding Author
Fangyuan Liu
Available Online May 2017.
DOI
10.2991/icmeit-17.2017.69How to use a DOI?
Keywords
particle swarm optimization; biogeography-based optimization; abnormal breast; identification; classification; detection.
Abstract

The breast cancer is the most common cancer among women. To detect it in an accurate way, we designed a new abnormal breast detection system based on the hybridization of particle swarm optimization and biogeography-based optimization. The simulation results showed the sensitivity achieved 87.90ñ0.88%, the specificity achieved 87.20 ñ2.74%, and the accuracy achieved 87.55 ñ1.34%. Our method is better than two state-of-the-art methods.

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)

Volume Title
Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
Series
Advances in Computer Science Research
Publication Date
May 2017
ISBN
978-94-6252-338-8
ISSN
2352-538X
DOI
10.2991/icmeit-17.2017.69How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Fangyuan Liu
AU  - Koji Nakamura
AU  - Rodney Payne
PY  - 2017/05
DA  - 2017/05
TI  - Abnormal Breast Detection Via Combination of Particle Swarm Optimization and Biogeography-Based Optimization
BT  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 356
EP  - 360
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.69
DO  - 10.2991/icmeit-17.2017.69
ID  - Liu2017/05
ER  -