Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization

Abstract

(Background) We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over Dataset-66, Dataset-160, and Dataset-255, respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.