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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhou, X.-X.: Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation 92(9), 861–871 (2016). https://doi.org/10.1177/0037549716666962
Dong, Z.: Synthetic minority oversampling technique and fractal dimension for identifying multiple sclerosis. Fractals 25(4) (2017). Article ID: 1740010
Cheng, H.: Multiple sclerosis identification based on fractional Fourier entropy and a modified Jaya algorithm. Entropy 20(4) (2018). Article ID: 254
Huang, C.: Multiple sclerosis identification by 14-layer convolutional neural network with batch normalization, dropout, and stochastic pooling. Front. Neurosci. 12 (2018). Article ID: 818. https://doi.org/10.3389/fnins.2018.00818
Zhan, T.M., Chen, Y.: Multiple sclerosis detection based on biorthogonal wavelet transform, RBF kernel principal component analysis, and logistic regression. IEEE Access 4, 7567–7576 (2016). https://doi.org/10.1109/ACCESS.2016.2620996
Azadmehr, A., et al.: Immunomodulatory and anti-inflammatory effects of Scrophularia megalantha Ethanol extract on an experimental model of multiple sclerosis. Res. J. Pharmacognosy 6(1), 43–50 (2019). https://doi.org/10.22127/rjp.2018.80370
Guo, Y., Qin, P.L.: Research on detection algorithm of multiple sclerosis of brain based on multimode local steering nucleus. Comput. Sci. 45(3), 243–248 (2018)
Lopez, M.: Multiple sclerosis slice identification by Haar wavelet transform and logistic regression. Adv. Eng. Res. 114, 50–55 (2017)
Pan, C.: Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. J. Comput. Sci. 28, 1–10 (2018). https://doi.org/10.1016/j.jocs.2018.07.003
MRI Lesion Segmentation in Multiple Sclerosis Database (2018). eHealth laboratory, University of Cyprus
Wang, S., Chen, Y.: Fruit category classification via an eight-layer convolutional neural network with parametric rectified linear unit and dropout technique. Multimedia Tools Appl. (2018). https://doi.org/10.1007/s11042-018-6661-6
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). https://doi.org/10.1016/j.jocs.2018.05.005
Zhao, G.: Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units. J. Real-Time Image Proc. 15(3), 631–642 (2018). https://doi.org/10.1007/s11554-017-0717-0
Sangaiah, A.K.: Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-018-3924-0
Xie, S.: Alcoholism identification based on an AlexNet transfer learning model. Front. Psychiatry (2019). https://doi.org/10.3389/fpsyt.2019.00205
Sik, H.H., Gao, J.L., Fan, J.C., Wu, B.W.Y., Leung, H.K., Hung, Y.S.: Using wavelet entropy to demonstrate how mindfulness practice increases coordination between irregular cerebral and cardiac activities. Jove-J. Visualized Exp. (123), 10 (2017). Article ID: e55455. https://doi.org/10.3791/55455
Gorriz, J.M.: Multivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization. J. Alzheimers Dis. 65(3), 855–869 (2018). https://doi.org/10.3233/JAD-170069
Li, Y.-J.: Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimedia Tools Appl. 77(9), 10393–10417 (2018). https://doi.org/10.1007/s11042-016-4222-4
Han, L.: Identification of Alcoholism based on wavelet Renyi entropy and three-segment encoded Jaya algorithm. Complexity (2018). Article ID: 3198184
Phillips, P.: Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm. Neurocomputing 272, 668–676 (2018). https://doi.org/10.1016/j.neucom.2017.08.015
Li, P., Liu, G.: Pathological brain detection via wavelet packet Tsallis entropy and real-coded biogeography-based optimization. Fundamenta Informaticae 151(1–4), 275–291 (2017)
Guliyev, N.J., Ismailov, V.E.: Approximation capability of two hidden layer feedforward neural networks with fixed weights. Neurocomputing 316, 262–269 (2018). https://doi.org/10.1016/j.neucom.2018.07.075
Naggaz, N.: Remote-sensing image classification based on an improved probabilistic neural network. Sensors 9(9), 7516–7539 (2009)
Zhang, Y.: Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Syst. Appl. 36(5), 8849–8854 (2009)
Wu, L.: Weights optimization of neural network via improved BCO approach. Prog. Electromagnet. Res. 83, 185–198 (2008). https://doi.org/10.2528/PIER08051403
Wu, L.: Crop classification by forward neural network with adaptive chaotic particle swarm optimization. Sensors 11(5), 4721–4743 (2011)
Ji, G.: Fruit classification using computer vision and feedforward neural network. J. Food Eng. 143, 167–177 (2014). https://doi.org/10.1016/j.jfoodeng.2014.07.001
Ji, G.: Genetic pattern search and its application to brain image classification. Math. Probl. Eng. (2013). Article ID: 580876. https://doi.org/10.1155/2013/580876
Ji, G.L.: A rule-based model for bankruptcy prediction based on an improved genetic ant colony algorithm. Math. Prob. Eng. (2013). Article ID: 753251. https://doi.org/10.1155/2013/753251
Nurcahyo, S., Nhita, F., Adiwijaya, K.: Rainfall prediction in Kemayoran Jakarta using hybrid Genetic Algorithm (GA) and Partially Connected Feedforward Neural Network (PCFNN). In: 2nd International Conference on Information and Communication Technology (ICOICT), Bandung, Indonesia, pp. 166–171. IEEE (2014)
Gagnon, R., Gosselin, L., Park, S., Stratbucker, S., Decker, S.: Comparison between two genetic algorithms minimizing carbon footprint of energy and materials in a residential building. J. Build. Perform. Simul. 12(2), 224–242 (2019). https://doi.org/10.1080/19401493.2018.1501095
Wang, S., Wu, L., Huo, Y., Wu, X., Wang, H., Zhang, Y.: Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm. In: Yin, H., et al. (eds.) IDEAL 2016. LNCS, vol. 9937, pp. 10–17. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46257-8_2
Wei, L., Yang, J.: Fitness-scaling adaptive genetic algorithm with local search for solving the Multiple Depot Vehicle Routing Problem. Simulation 92(7), 601–616 (2016). https://doi.org/10.1177/0037549715603481
Kerr, A., Mullen, K.: A comparison of genetic algorithms and simulated annealing in maximizing the thermal conductance of harmonic lattices. Comput. Mater. Sci. 157, 31–36 (2019). https://doi.org/10.1016/j.commatsci.2018.10.007
Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man Cybern. 24(4), 656–667 (1994)
Li, J.: Texture analysis method based on fractional Fourier entropy and fitness-scaling adaptive genetic algorithm for detecting left-sided and right-sided sensorineural hearing loss. Fundamenta Informaticae 151(1–4), 505–521 (2017)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-20518-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20517-1
Online ISBN: 978-3-030-20518-8
eBook Packages: Computer ScienceComputer Science (R0)