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Smart pathological brain detection system by predator-prey particle swarm optimization and single-hidden layer neural-network

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Abstract

In order to develop an artificial intelligence and computer-aided diagnosis system that assists neuroradiologists to interpret magnetic resonance (MR) images. This paper employed Hu moment invariant (HMI) as the brain image features, and we proposed a novel predator-prey particle swarm optimization (PP-PSO) algorithm used to train the weights of single-hidden layer neural-network (SLN). We used five-fold stratified cross validation (FFSCV) for statistical analysis. Our proposed HMI + SLN + PP-PSO method achieved a sensitivity of 96.00 ± 5.16%, a specificity of 98.57 ± 0.75%, and an accuracy of 98.25 ± 0.65% for the DA-160 dataset, and yields a sensitivity of 97.14 ± 2.33%, a specificity of 97.00 ± 0.34%, and an accuracy of 97.02 ± 0.33% for the DA-255 dataset. Our method performs better than six state-of-the-art approaches.

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Authors and Affiliations

  1. School of Computer Science and Technology & School of Education Science, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Hainan Wang, Yiding Lv & Zhihai Lu

  2. Department of Neurology, First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China

    Hong Chen

  3. School of Information Engineering, Yangzhou University, Yangzhou, China

    Yujie Li

  4. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, Hubei, 430073, China

    Yin Zhang

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  1. Hainan Wang
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  2. Yiding Lv
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  3. Hong Chen
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  4. Yujie Li
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Correspondence to Yujie Li, Yin Zhang or Zhihai Lu.

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The original version of this article was revised: The white background of “44” in Fig. 3b should be removed.

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Wang, H., Lv, Y., Chen, H. et al. Smart pathological brain detection system by predator-prey particle swarm optimization and single-hidden layer neural-network. Multimed Tools Appl 77, 3871–3885 (2018). https://doi.org/10.1007/s11042-016-4242-0

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  • DOI: https://doi.org/10.1007/s11042-016-4242-0

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