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Adaptive Neuro-Fuzzy Inference System-Based Chaotic Swarm Intelligence Hybrid Model for Recognition of Mild Cognitive Impairment from Resting-State fMRI

  • Ahmed M. AnterEmail author
  • Zhiguo Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11843)

Abstract

Individuals diagnosed with mild cognitive impairment (MCI) are at high risk of transition to Alzheimer’s disease (AD). Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising neuroimaging technique for identifying patients with MCI. In this paper, a new hybrid model based on Chaotic Binary Grey Wolf Optimization Algorithm (CBGWO) and Adaptive Neuro-fuzzy Inference System (ANFIS) is proposed; namely (CBGWO-ANFIS) to diagnose the MCI. The proposed model is applied on real dataset recorded by ourselves and the process of diagnosis is comprised of five main phases. Firstly, the fMRI data are preprocessed by sequence of steps to enhance data quality. Secondly, features are extracted by localizing 160 regions of interests (ROIs) from the whole-brain by overlapping the Dosenbach mask, and then fractional amplitude of low-frequency fluctuation (fALFF) of the signals inside ROIs is estimated and used to represent local features. Thirdly, feature selection based non-linear GWO, chaotic map and naive Bayes (NB) are used to determine the significant ROIs. The NB criterion is used as a part of the kernel function in the GWO. CBGWO attempts to reduce the whole feature set without loss of significant information to the prediction process. Chebyshev map is used to estimate and tune GWO parameters. Fourthly, an ANFIS method is utilized to diagnose MCI. Fifthly, the performance is evaluated using different statistical measures and compared with different met-heuristic algorithms. The overall results indicate that the proposed model shows better performance, lower error, higher convergence speed and shorter execution time with accuracy reached to  86%.

Keywords

rs-fMRI MCI Optimization Swarm intelligence ANFS Chaos theory 

References

  1. 1.
    Vieira, S., Pinaya, W.H., Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci. Biobehav. Rev. 74, 58–75 (2017)CrossRefGoogle Scholar
  2. 2.
    Suk, H.I., Wee, C.Y., Lee, S.W., Shen, D.: State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage 129, 292–307 (2016)CrossRefGoogle Scholar
  3. 3.
    Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer’s disease diagnostics by adaptation of 3D convolutional network. In 2016 IEEE International Conference on Image Processing (ICIP), pp. 126–130, September 2016Google Scholar
  4. 4.
    Han, X., Zhong, Y., He, L., Yu, P.S., Zhang, L.: The unsupervised hierarchical convolutional sparse auto-encoder for neuroimaging data classification. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds.) BIH 2015. LNCS (LNAI), vol. 9250, pp. 156–166. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23344-4_16CrossRefGoogle Scholar
  5. 5.
    Kim, J., Calhoun, V.D., Shim, E., Lee, J.H.: Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage 124, 127–146 (2016)CrossRefGoogle Scholar
  6. 6.
    Grothe, M., Heinsen, H., Teipel, S.: Longitudinal measures of cholinergic forebrain atrophy in the transition from healthy aging to Alzheimer’s disease. Neurobiol. Aging 34(4), 1210–1220 (2013)CrossRefGoogle Scholar
  7. 7.
    Li, Y., et al.: Abnormal resting-state functional connectivity strength in mild cognitive impairment and its conversion to Alzheimer’s disease. Neural Plast. 2016, 4680972 (2016).  https://doi.org/10.1155/2016/4680972CrossRefGoogle Scholar
  8. 8.
    Faris, H., Aljarah, I., Al-Betar, M.A., Mirjalili, S.: Grey wolf optimizer: a review of recent variants and applications. Neural Comput. Appl. 30(2), 413–435 (2018)CrossRefGoogle Scholar
  9. 9.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  10. 10.
    Li, T., Li, J., Liu, Z., Li, P., Jia, C.: Differentially private naive bayes learning over multiple data sources. Inf. Sci. 444, 89–104 (2018)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hasanipanah, M., Amnieh, H.B., Arab, H., Zamzam, M.S.: Feasibility of PSO-ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput. Appl. 30(4), 1015–1024 (2018)CrossRefGoogle Scholar
  12. 12.
    Alam, S., Dobbie, G., Koh, Y.S., Riddle, P., Rehman, S.U.: Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol. Comput. 17, 1–13 (2014) CrossRefGoogle Scholar
  13. 13.
    Anter, A., Gupta, D., Castillo, O.: novel parameter estimation in dynamic model via fuzzy swarm intelligence and chaos theory for faults in wastewater treatment plant. Soft Comput. 1–19 (2019). https://doi.org/10.1007/s00500-019-04225-7
  14. 14.
    ADNI. http://adni.loni.usc.edu/. Accessed April 2019
  15. 15.
    Mafarja, M., Eleyan, D., Abdullah, S., Mirjalili, S.: S-shaped vs. V-shaped transfer functions for ant lion optimization algorithm in feature selection problem. In: Proceedings of the International Conference on Future Networks and Distributed Systems, p. 14. ACM, July 2017Google Scholar
  16. 16.
    Anter, A., Ali, M.: Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems. Soft Comput. 1–20 (2019)Google Scholar
  17. 17.
    Anter, A.M., Azar, A.T., Fouad, K.M.: Intelligent hybrid approach for feature selection. In: Hassanien, A.E., Azar, A.T., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds.) AMLTA 2019. AISC, vol. 921, pp. 71–79. Springer, Cham (2020).  https://doi.org/10.1007/978-3-030-14118-9_8CrossRefGoogle Scholar
  18. 18.
    Rodrigues, D., et al.: A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syst. Appl. 41(5), 2250–2258 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Biomedical Engineering, Health Science CenterShenzhen UniversityShenzhenChina
  2. 2.Faculty of Computers and InformationBeni-Suef UniversityBenisuefEgypt

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