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Exploring a smart pathological brain detection method on pseudo Zernike moment

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Abstract

Pathological brain detection by computer vision is now attracting intense attentions from academic fields. Nevertheless, most of recent methods suffer from low-accuracy. This study combined two successful techniques: pseudo Zernike moment and kernel support vector machine. Three open datasets were downloaded and used. The 10 times of K-fold stratified cross validation showed our method using 19-order pseudo Zernike moments achieved perfect classification on the first dataset. It achieved a sensitivity of 99.93 ± 0.23%, a specificity of 98.50 ± 2.42%, and an accuracy of 99.75 ± 0.32% on the second dataset. It achieved a sensitivity of 99.64 ± 0.42%, a specificity of 98.29 ± 2.76%, and an accuracy of 99.45 ± 0.38% on the third dataset. This approach performs better than eleven state-of-the-art smart pathological brain detection methods.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61602250), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Natural Science Foundation of Jiangsu Province (BK20150983), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Opening Project of State Key Laboratory of Digital Publishing Technology.

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Author notes

  1. Yu-Dong Zhang, Yongyan Jiang and Weiguo Zhu contributed equally to this work.

Authors and Affiliations

  1. Jiangsu Key Laboratory of Advanced Manufacturing Technology, Huaiyin, Jiangsu, 223003, China

    Yu-Dong Zhang & Weiguo Zhu

  2. Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Policy Academy, Changsha, Hunan, 410138, China

    Yu-Dong Zhang

  3. School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Yu-Dong Zhang & Siyuan Lu

  4. College of Science, Zhongyuan University of Technology, Zhengzhou, 450007, China

    Yongyan Jiang

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

    Guihu Zhao

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  1. Yu-Dong Zhang
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  2. Yongyan Jiang
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  3. Weiguo Zhu
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  4. Siyuan Lu
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  5. Guihu Zhao
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Correspondence to Siyuan Lu or Guihu Zhao.

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Zhang, YD., Jiang, Y., Zhu, W. et al. Exploring a smart pathological brain detection method on pseudo Zernike moment. Multimed Tools Appl 77, 22589–22604 (2018). https://doi.org/10.1007/s11042-017-4703-0

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