Skip to main content
SpringerLink
Log in

Multi-classification of Alzheimer’s Disease by NSGA-II Slices Optimization and Fusion Deep Learning

  • Conference paper
  • First Online:
Artificial Life and Evolutionary Computation (WIVACE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1977))

Included in the following conference series:

  • 31 Accesses

Abstract

This paper presents a two-phase hierarchical classifier for determining the different states in Alzheimer’s disease (AD). In the first phase, an evolutionary system is developed to determine the most relevant slices (in both X-axis and Y-axis) of the magnetic resonance imaging (MRI) for the construction of a classifier. To obtain the image features, the biorthogonal wavelet transform 3.3 was used at level 2. Due to the high number of coefficients, a dimensionality reduction is performed using minimum Redundancy - Maximum Relevance algorithm (mRMR) and Principal Component Analysis (PCA). An evolutionary algorithm on a high-performance computer with GPU was used to optimize the slides. Support vector machine (SVM) was used in the fitness function to estimate the features of the classifier in a computationally simple way. In the second phase, using the different solutions of the Pareto front obtained by the evolutionary algorithm, a multiple deep learning system was developed, each of the systems having as input one of the selected slices of the analyzed solution. The solution with three slices (trade-off between complexity and accuracy) was used as the solution. The obtained hierarchical deep learning system fused the information from each system and analyzed the probabilities obtained for each class. As a final result, an accuracy of 92% was obtained for the six classes. A total of 1,200 patients from the Alzheimer’s disease neuroimaging initiative (ADNI) database were used, corresponding to six different classes of patients (with varying degrees of dementia).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Querfurth, H.W., LaFerla, F.M.: Alzheimer’s disease. N. Engl. J. Med. 362(4), 329–344 (2010)

    Article  Google Scholar 

  2. GBD 2019 Dementia Forecasting Collaborators, “Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019,” Lancet Public Health (2022)

    Google Scholar 

  3. “Global dementia cases forecasted to triple by 2050,” Alzheimer’s Association International Conference®(AAIC®) (2021)

    Google Scholar 

  4. Leifer, B.P.: Alzheimer’s disease: seeing the signs early. J. Am. Acad. Nurse Pract. 21(11), 588–595 (2009)

    Article  Google Scholar 

  5. Barczak, A.: Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland, “The early diagnosis of alzheimer’s disease”, Pediatr. Med. Rodz., vol. 14(2), 157–166 (2018). Department of Neurodegenerative Disorders

    Google Scholar 

  6. Jonin, P.-Y., et al.: Building memories on prior knowledge: behavioral and fMRI evidence of impairment in early Alzheimer’s disease. Neurobiol. Aging 110, 1–12 (2022)

    Article  Google Scholar 

  7. Selkoe, D.J.: Early network dysfunction in Alzheimer’s disease. Science 365(6453), 540–541 (2019)

    Article  Google Scholar 

  8. Li, X., Coyle, D., Maguire, L., Watson, D.R., McGinnity, T.M.: Gray matter concentration and effective connectivity changes in Alzheimer’s disease: a longitudinal structural MRI study. Neuroradiology 53(10), 733–748 (2011)

    Article  Google Scholar 

  9. Odusami, M., Maskeliūnas, R., Damaševičius, R., Krilavičius, T.: Analysis of features of Alzheimer’s disease: detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network. Diagnostics (Basel) 11(6), 1071 (2021)

    Article  Google Scholar 

  10. Li, Z., Jiang, X., Wang, Y., Kim, Y.: Applied machine learning in Alzheimer’s disease research: omics, imaging, and clinical data. Emerg. Top. Life Sci. 5(6), 765–777 (2021)

    Article  Google Scholar 

  11. Wu, H., Luo, J., Lu, X., Zeng, Y.: 3D transfer learning network for classification of Alzheimer’s disease with MRI. Int. J. Mach. Learn. Cybern. 13, 1997–2011 (2022)

    Article  Google Scholar 

  12. Zamani, J., Sadr, A., Javadi, A.-H.: Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data. Sci. Rep. 12(1), 1020 (2022)

    Article  Google Scholar 

  13. Sun, Z., Qiao, Y., Lelieveldt, B.P., Staring, M., Alzheimer’s disease neuroimaging initiative.: integrating spatial-anatomical regularization and structure sparsity into SVM: improving interpretation of Alzheimer’s disease classification. NeuroImage, 178, 445–460 (2018)

    Google Scholar 

  14. Dukart, J., et al.: Meta-analysis based SVM classification enables accurate detection of Alzheimer’s disease across different clinical centers using FDG-PET and MRI. Psychiatry Res. 212(3), 230–236 (2013)

    Article  Google Scholar 

  15. Tantiwetchayanon, K., Vichianin, Y., Ekjeen, T., Srungboonmee, K., Ngamsombat, C., Chawalparit, O.: Comparison of the WEKA and SVM-light based on support vector machine in classifying Alzheimer’s disease using structural features from brain MR imaging. J. Phys: Conf. Ser. 1248(1), 012003 (2019)

    Google Scholar 

  16. Romano, M.F., Kolachalama, V.B.: Deep learning for subtyping the Alzheimer’s disease spectrum. Trends Mol. Med. 28, 81–83 (2022)

    Article  Google Scholar 

  17. Lei, B., et al.: Predicting clinical scores for Alzheimer’s disease based on joint and deep learning. Expert Syst. Appl. 187(115966), 115966 (2022)

    Article  Google Scholar 

  18. Dai, Y., Bai, W., Tang, Z., Xu, Z., Chen, W.: Computer-aided diagnosis of Alzheimer’s disease via deep learning models and radiomics method. Appl. Sci. (Basel) 11(17), 8104 (2021)

    Article  Google Scholar 

  19. Helaly, H.A., Badawy, M., Haikal, A.Y.: Deep learning approach for early detection of Alzheimer’s disease. Cognit. Comput. 14, 1–17 (2021)

    Google Scholar 

  20. Ashburner, J.: SPM: a history. NeuroImage 62(2), 791–800 (2012). https://doi.org/10.1016/j.neuroimage.2011.10.025

    Article  Google Scholar 

  21. Shen, J.: Tools for NIfTI and analyze image. MATLAB Central File Exchange (2023)

    Google Scholar 

  22. Chaplot, S., Patnaik, L., Jagannathan, N.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Sig. Process. Control. 1(1), 86–92 (2006). https://doi.org/10.1016/j.bspc.2006.05.002

    Article  Google Scholar 

  23. Feng, J., Zhang, S.-W., Chen, L.: Identification of Alzheimer’s disease based on wavelet transformation energy feature of the structural MRI image and NN classifier. Artif. Intell. Med. 108, 101940 (2020). https://doi.org/10.1016/j.artmed.2020.101940

    Article  Google Scholar 

  24. Gilles, J.: Empirical wavelet transform. IEEE Trans. Sig. Process. 61(16), 3999–4010 (2013). https://doi.org/10.1109/tsp.2013.2265222

    Article  MathSciNet  Google Scholar 

  25. Wang, X.H., Zhao, B., Li, L.: Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI. Front. Neurosci. 16, 1038514 (2022). https://doi.org/10.3389/fnins.2022.1038514

    Article  Google Scholar 

  26. Radovic, M., Ghalwash, M., Filipovic, N., Obradovic, Z.: Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform. 18, 1–14 (2017). https://doi.org/10.1186/s12859-016-1423-9

    Article  Google Scholar 

  27. Qu, K., Xu, J., Han, Z., Xu, S.: Maximum relevance minimum redundancy-based feature selection using rough mutual information in adaptive neighborhood rough sets. Appl. Intell. 53(14), 17727–17746 (2023). https://doi.org/10.1007/s10489-022-04398-z

    Article  Google Scholar 

  28. Drucker, H., Wu, D., Vapnik, V.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048–1054 (1999)

    Article  Google Scholar 

  29. Devos, O., Downey, G., Duponchel, L.: Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils. Food Chem. 148, 124–130 (2014)

    Article  Google Scholar 

  30. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  31. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2016)

    Google Scholar 

  32. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  33. Dubey, A.K., Jain, V.: Automatic facial recognition using VGG16 based transfer learning model. J. Inf. Optim. Sci. 41(7), 1589–1596 (2020)

    Google Scholar 

Download references

Acknowledgements

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Project PID2021-128317OB-I00 and the projects from Junta de Andalucia P20-00163.

Author information

Authors and Affiliations

  1. ETSIIT, University of Granada, Granada, Spain

    Ignacio Rojas-Valenzuela & Ignacio Rojas

  2. Department of Economics and Statistics, University of Leon, Leon, Spain

    Elvira Delgado-Marquez

  3. Faculty of Science, University of Granada, Granada, Spain

    Olga Valenzuela

Authors
  1. Ignacio Rojas-Valenzuela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ignacio Rojas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elvira Delgado-Marquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olga Valenzuela
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ignacio Rojas-Valenzuela .

Editor information

Editors and Affiliations

  1. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

  2. University of Parma, Parma, Italy

    Stefano Cagnoni

  3. University of Modena and Reggio Emilia, Modena, Italy

    Roberto Serra

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rojas-Valenzuela, I., Rojas, I., Delgado-Marquez, E., Valenzuela, O. (2024). Multi-classification of Alzheimer’s Disease by NSGA-II Slices Optimization and Fusion Deep Learning. In: Villani, M., Cagnoni, S., Serra, R. (eds) Artificial Life and Evolutionary Computation. WIVACE 2023. Communications in Computer and Information Science, vol 1977. Springer, Cham. https://doi.org/10.1007/978-3-031-57430-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-57430-6_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57429-0

  • Online ISBN: 978-3-031-57430-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation