Feed-forward Neural Network Optimized by Hybridization of PSO and ABC for Abnormal Brain Detection.pdf
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Feed-forward Neural Network Optimized by Hybridization of PSO and ABC for Abnormal Brain Detection
Feed-Forward Neural Network Optimized by
Hybridization of PSO and ABC for Abnormal Brain
Detection
Shuihua Wang,1,2,3 Yudong Zhang,1,3 Zhengchao Dong,4 Sidan Du,2 Genlin Ji,1 Jie Yan,5
Jiquan Yang,3 Qiong Wang,3 Chunmei Feng,3 Preetha Phillips6
1 School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu
210023, China
2 School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046,
China
3 Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042,
China
4 Translational Imaging Division and MRI Unit, Columbia University and New York State Psychiatric
Institute, New York, NY 10032
5 Department of Applied Physics, Stanford University, Stanford, CA 94305
6 School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV 25443
Received 20 February 2015; revised 19 March 2015; accepted 7 April 2015
ABSTRACT: Automated and accurate classification of MR brain
images is of crucially importance for medical analysis and interpreta-
tion. We proposed a novel automatic classification system based on
particle swarm optimization (PSO) and artificial bee colony (ABC),
with the aim of distinguishing abnormal brains from normal brains in
MRI scanning. The proposed method used stationary wavelet trans-
form (SWT) to extract features from MR brain images. SWT is
translation-invariant and performed well even the image suffered
from slight translation. Next, principal component analysis (PCA) was
harnessed to reduce the SWT coefficients. Based on three different
hybridization methods of PSO and ABC, we proposed three new var-
iants of feed-forward neural network (FNN), consisting of IABAP-
FNN, ABC-SPSO-FNN, and HPA-FNN. The 10 runs of K-fold cross
validation result showed the proposed HPA-FNN was superior to not
only other two proposed classifiers but also existing state-of-the-art
methods in terms of classification accuracy. In addition, the method
achieved perfect classification
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