volume | PIER Journals

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 tumors from normal brains in MRI scanning. (Methods) 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×K-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. Moreover, 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 0.053 s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieved nearly perfect detection on tumors in MRI scanning.

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Prof. Yudong Zhang

Dr. Shuihua Wang

Dr. Zhengchao Dong

Dr. Preetha Phillip

Dr. Genlin Ji

Dr. Jiquan Yang