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Multiple Sclerosis Detection via Wavelet Entropy and Feedforward Neural Network Trained by Adaptive Genetic Algorithm

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Book cover Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11507))

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Abstract

Multiple sclerosis is a disease that damages the central nervous system. Current medical treatments can only prevent or relieve symptoms. The target of this study is to improve the detection efficiency and classification accuracy. We propose a method based on wavelet entropy and feedforward neural network trained by adaptive genetic algorithm that is implemented over 10 runs of 10-fold cross validation. In which the wavelet entropy serves as a feature extractor and the feedforward neural network is employed as a classifier. Adaptive genetic algorithm work as a training algorithm. We also use the three-level decomposition of db2 wavelet to make a frequency analysis. According to the experimental results, the global optimization capability of adaptive genetic algorithm is more powerful than ordinary genetic algorithm. Comparing to the HWT-LR method, the accuracy of our method detection is higher.

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Acknowledgement

This paper was supported by National Natural Science Foundation of China (No. 61503124), key scientific and technological project of Henan province (No. 172102210273, 182102210086).

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Authors and Affiliations

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

    Ji Han & 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. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Han, J., Hou, SM. (2019). Multiple Sclerosis Detection via Wavelet Entropy and Feedforward Neural Network Trained by Adaptive Genetic Algorithm. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_8

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20517-1

  • Online ISBN: 978-3-030-20518-8

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