Abstract
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.