TY - JOUR
T1 - Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection
AU - Wang, Shuihua
AU - Zhang, Yudong
AU - Dong, Zhengchao
AU - Du, Sidan
AU - Ji, Genlin
AU - Yan, Jie
AU - Yang, Jiquan
AU - Wang, Qiong
AU - Feng, Chunmei
AU - Phillips, Preetha
N1 - Publisher Copyright:
© 2015 Wiley Periodicals, Inc.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. 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 transform (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 variants 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 on Dataset-66 and Dataset-160. For Dataset-255, the 10 repetition achieved average sensitivity of 99.37%, average specificity of 100.00%, average precision of 100.00%, and average accuracy of 99.45%. The offline learning cost 219.077 s for Dataset-255, and merely 0.016 s for online prediction. Thus, the proposed SWT-+-PCA-+-HPA-FNN method excelled existing methods. It can be applied to practical use.
AB - Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. 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 transform (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 variants 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 on Dataset-66 and Dataset-160. For Dataset-255, the 10 repetition achieved average sensitivity of 99.37%, average specificity of 100.00%, average precision of 100.00%, and average accuracy of 99.45%. The offline learning cost 219.077 s for Dataset-255, and merely 0.016 s for online prediction. Thus, the proposed SWT-+-PCA-+-HPA-FNN method excelled existing methods. It can be applied to practical use.
KW - artificial bee colony
KW - classification
KW - feed-forward neural network
KW - hybridization
KW - magnetic resonance imaging
KW - particle swarm optimization
KW - pattern recognition
KW - principle component analysis
KW - stationary wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=84929610466&partnerID=8YFLogxK
U2 - 10.1002/ima.22132
DO - 10.1002/ima.22132
M3 - Article
AN - SCOPUS:84929610466
SN - 0899-9457
VL - 25
SP - 153
EP - 164
JO - International Journal of Imaging Systems and Technology
JF - International Journal of Imaging Systems and Technology
IS - 2
ER -