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PIER PIER B PIER C PIER M PIER Letters
2015-06-26
Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization
By
Yudong Zhang,

    Prof. Yudong Zhang

    Department of Psychiatry

    Columbia University

    USA

    Homepage

Shuihua Wang,

    Dr. Shuihua Wang

    School of Computer Science and Technology

    Nanjing Normal University

    China

    Homepage

Zhengchao Dong,

    Dr. Zhengchao Dong

    Department of Psychiatry

    Columbia University

    USA

    Homepage

Preetha Phillip,

    Dr. Preetha Phillip

    School of Natural Sciences and Mathematics

    Shepherd University

    USA

    Homepage

Genlin Ji,

    Dr. Genlin Ji

    School of Computer Science and Technology

    Nanjing Normal University

    China

    Homepage

Jiquan Yang

    Dr. Jiquan Yang

    Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing

    China

    Homepage

Progress In Electromagnetics Research, Vol. 152, 41-58, 2015
doi:10.2528/PIER15040602
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.
Citation
Yudong Zhang, Shuihua Wang, Zhengchao Dong, Preetha Phillip, Genlin Ji, and Jiquan Yang, "Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization," Progress In Electromagnetics Research, Vol. 152, 41-58, 2015.
doi:10.2528/PIER15040602
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