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Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm

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Abstract

Pathological brain detection is an automated computer-aided diagnosis for brain images. This study provides a novel method to achieve this goal.We first used synthetic minority oversampling to balance the dataset. Then, our system was based on three components: wavelet packet Tsallis entropy, extreme learning machine, and Jaya algorithm. The 10 repetitions of K-fold cross validation showed our method achieved perfect classification on two small datasets, and achieved a sensitivity of 99.64 ± 0.52%, a specificity of 99.14 ± 1.93%, and an accuracy of 99.57 ± 0.57% over a 255-image dataset. Our method performs better than six state-of-the-art approaches. Besides, Jaya algorithm performs better than genetic algorithm, particle swarm optimization, and bat algorithm as ELM training method.

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Acknowledgements

The paper is supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601).

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Author notes

  1. Yu-Dong Zhang, Guihu Zhao and Junding Sun contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, People’s Republic of China

    Yu-Dong Zhang, Junding Sun, Xiaosheng Wu, Zhi-Heng Wang & Hong-Min Liu

  2. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Guihu Zhao

  3. Department of Instrumentation and Control Engineering, Kalasalingam University, Srivilliputtur, Virudhunagar, Tamil Nadu, India

    Vishnu Varthanan Govindaraj

  4. School of Technology, Nanjing Audit University, Nanjing, Jiangsu, 211815, China

    Tianmin Zhan

  5. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Jianwu Li

Authors
  1. Yu-Dong Zhang
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  2. Guihu Zhao
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  3. Junding Sun
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  4. Xiaosheng Wu
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  5. Zhi-Heng Wang
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  6. Hong-Min Liu
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  7. Vishnu Varthanan Govindaraj
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  8. Tianmin Zhan
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  9. Jianwu Li
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Contributions

Y Zhang & G Zhao conceived the study. J Sun & T Zhan designed the model. Y Zhang & J Li acquired the data. Y Zhang, T Zhan, J Li analyzed the data. G Zhao interpreted the data. Y Zhang, X Wu, Z Wang, H Liu, V Govindaraj developed the programs. Y Zhang & T Zhan wrote the draft. All the authors gave critical revisions and approved the submission.

Corresponding authors

Correspondence to Yu-Dong Zhang, Vishnu Varthanan Govindaraj, Tianmin Zhan or Jianwu Li.

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We declared there is no conflict of interest in terms with this submission.

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Zhang, YD., Zhao, G., Sun, J. et al. Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm. Multimed Tools Appl 77, 22629–22648 (2018). https://doi.org/10.1007/s11042-017-5023-0

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  • DOI: https://doi.org/10.1007/s11042-017-5023-0

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