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A Multiple Sclerosis Recognition via Hu Moment Invariant and Artificial Neural Network Trained by Particle Swarm Optimization

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Multimedia Technology and Enhanced Learning (ICMTEL 2020)

Abstract

Multiple sclerosis can damage the central nervous system, and current drugs are difficult to completely cure symptoms. The aim of this paper was to use deep learning methods to increase the detection rate of multiple sclerosis, thereby increasing the patient’s chance of treatment. We presented a new method based on hu moment invariant and artificial neural network trained by particle swarm optimization. Our method was carried out over ten runs of ten-fold cross validation. The experimental results show that the optimization ability of particle swarm optimization algorithm is superior to the genetic algorithm, simulated annealing algorithm and immune genetic algorithm. At the same time, compared with the HWT+PCA+LR method and the WE-FNN-AGA method, our method performs better in the performance of the detection.

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References

  1. Barro, C., et al.: Serum neurofilament as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis. Brain 141(8), 2382–2391 (2018)

    Article  Google Scholar 

  2. Tintore, M., et al.: Treatment of multiple sclerosis—success from bench to bedside. Nat. Rev. Neurol. 15(1), 53–58 (2019)

    Article  Google Scholar 

  3. Pietroboni, A.M., et al.: The loss of macular ganglion cells begins from the early stages of disease and correlates with brain atrophy in multiple sclerosis patients. Mult. Scler. J. 25(1), 31–38 (2019)

    Article  Google Scholar 

  4. Alshayeji, M.H., et al.: An efficient multiple sclerosis segmentation and detection system using neural networks. Comput. Electr. Eng. 71, 191–205 (2018)

    Article  Google Scholar 

  5. Lopez, M.: Multiple sclerosis slice identification by haar wavelet transform and logistic regression. Adv. Eng. Res. 114, 50–55 (2017)

    Google Scholar 

  6. Han, J., Hou, S.-M.: Multiple sclerosis detection via wavelet entropy and feedforward neural network trained by adaptive genetic algorithm. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2019. LNCS, vol. 11507, pp. 87–97. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20518-8_8

    Chapter  Google Scholar 

  7. Govindaraj, V.V.: High performance multiple sclerosis classification by data augmentation and AlexNet transfer learning model. J. Med. Imaging Health Inform. 9(9), 2012–2021 (2019)

    Article  Google Scholar 

  8. Jiang, X.: Chinese sign language fingerspelling recognition via six-layer convolutional neural network with leaky rectified linear units for therapy and rehabilitation. J. Med. Imaging Health Inform. 9(9), 2031–2038 (2019)

    Article  Google Scholar 

  9. Yu, X., Zeng, N., Liu, S., Zhang, Y.-D.: Utilization of DenseNet201 for diagnosis of breast abnormality. Mach. Vis. Appl. 30(7–8), 1135–1144 (2019)

    Article  Google Scholar 

  10. Li, Z.: Teeth category classification via seven-layer deep convolutional neural network with max pooling and global average pooling. Int. J. Imaging Syst. Technol. 29, 577–583 (2019). https://doi.org/10.1002/ima.22337

    Article  Google Scholar 

  11. Hong, J.: Detecting cerebral microbleeds with transfer learning. Mach. Vis. Appl. 30(7–8), 1123–1133 (2019)

    Article  Google Scholar 

  12. Tang, C.: Cerebral micro-bleeding detection based on densely connected neural network. Front. Neurosci. 13, 422 (2019)

    Google Scholar 

  13. Xie, S.: Alcoholism identification based on an AlexNet transfer learning model. Front. Psychiatr. 10, 205 (2019)

    Google Scholar 

  14. Jia, W.: Five-category classification of pathological brain images based on deep stacked sparse autoencoder. Multimed. Tools Appl. 78(4), 4045–4064 (2017)

    Article  Google Scholar 

  15. Muhammad, K.: Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimed. Tools Appl. 78(3), 3613–3632 (2019)

    Article  Google Scholar 

  16. Sangaiah, A.K.: Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Comput. Appl. 32(3), 665–680 (2019). https://doi.org/10.1007/s00521-018-3924-0

    Article  Google Scholar 

  17. Pan, C.: Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. J. Comput. Sci. 28, 1–10 (2018)

    Article  MathSciNet  Google Scholar 

  18. Hou, X.-X.: Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimed. Tools Appl. 77(9), 10521–10538 (2018)

    Article  Google Scholar 

  19. Pan, C.: Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling. J. Comput. Sci. 27, 57–68 (2018)

    Article  Google Scholar 

  20. MRI Lesion Segmentation in Multiple Sclerosis Database, in eHealth Laboratory, University of Cyprus. http://www.medinfo.cs.ucy.ac.cy/index.php/downloads/datasets

  21. Wang, Y.Q., et al.: The optimal fractional S transform of seismic signal based on the normalized second-order central moment. J. Appl. Geophys. 129, 8–16 (2016)

    Article  Google Scholar 

  22. Yang, J.: Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. J. Exp. Theor. Artif. Intell. 29(2), 299–312 (2017)

    Article  Google Scholar 

  23. Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)

    Article  MATH  Google Scholar 

  24. Hou, X.-X.: Alcoholism detection by medical robots based on Hu moment invariants and predator-prey adaptive-inertia chaotic particle swarm optimization. Comput. Electr. Eng. 63, 126–138 (2017)

    Article  Google Scholar 

  25. Ramirez, J.: Unilateral sensorineural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression. Integr. Comput.-Aided Eng. 26, 411–426 (2019). https://doi.org/10.3233/ICA-190605

    Article  Google Scholar 

  26. Yang, J.: An adaptive encoding learning for artificial bee colony algorithms. J. Comput. Sci. 30, 11–27 (2019)

    Article  Google Scholar 

  27. Gorriz, J.M.: Multivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization. J. Alzheimer’s Dis. 65(3), 855–869 (2018)

    Article  Google Scholar 

  28. Sun, J.: Preliminary study on angiosperm genus classification by weight decay and combination of most abundant color index with fractional Fourier entropy. Multimed. Tools Appl. 77(17), 22671–22688 (2018)

    Article  Google Scholar 

  29. Zhao, G.: Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm. Multimed. Tools Appl. 77(17), 22629–22648 (2018)

    Article  Google Scholar 

  30. Lu, S.: Pathological brain detection in magnetic resonance imaging using combined features and improved extreme learning machines. J. Med. Imaging Health Informat. 8, 1486–1490 (2018)

    Article  Google Scholar 

  31. Li, Y.-J.: Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimed. Tools Appl. 77(9), 10393–10417 (2018)

    Article  Google Scholar 

  32. Kong, F.Q.: Ridge-based curvilinear structure detection for identifying road in remote sensing image and backbone in neuron dendrite image. Multimed. Tools Appl. 77(17), 22857–22873 (2018)

    Article  Google Scholar 

  33. Chowdhury, N.: A comparative analysis of feed-forward neural network & recurrent neural network to detect intrusion. In: 2008 International Conference on Electrical and Computer Engineering, pp. 488–492. IEEE (2008)

    Google Scholar 

  34. Eberhart, R., et al.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43 (1995)

    Google Scholar 

  35. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, Montreal, Canada, pp. 1137–1145 (1995)

    Google Scholar 

  36. Sharma, M., et al.: A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn. Lett. 94, 172–179 (2017)

    Article  Google Scholar 

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Acknowledgement

This paper was supported by Key research and development and technology promotion projects in Henan Province, China (No. 172102210273, 182102210086, 182102310629), Youth backbone training program for colleges and universities in Henan, China (No. 2018GGJS298).

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Authors and Affiliations

  1. Henan Polytechnic University, Jiaozuo, 454000, Henan, China

    Ji Han & Shou-Ming Hou

  2. Hebi Automotive Engineering Professional College, Hebi, 454030, Henan, China

    Shou-Ming Hou

Authors
  1. Ji Han
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  2. Shou-Ming Hou
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Correspondence to Shou-Ming Hou .

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Editors and Affiliations

  1. School of Informatics, University of Leicester, Leicestershire, UK

    Yu-Dong Zhang

  2. University of Leicester, Leicestershire, UK

    Shui-Hua Wang

  3. Human Normal University, Changsha, China

    Shuai Liu

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Han, J., Hou, SM. (2020). A Multiple Sclerosis Recognition via Hu Moment Invariant and Artificial Neural Network Trained by Particle Swarm Optimization. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-51103-6_22

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  • DOI: https://doi.org/10.1007/978-3-030-51103-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51102-9

  • Online ISBN: 978-3-030-51103-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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