Feed-forward Neural Network Optimized by Hybridization of PSO and ABC for Abnormal Brain Detection.pdfVIP

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