Skip to main content

Advertisement

SpringerLink
Log in

On the detection of Alzheimer’s disease using fuzzy logic based majority voter classifier

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is considered to be one of the most frequent neurogenerative dementia in the elderly population. In order to improve the quality of life span, early detection of AD has drawn significant attention to the researchers throughout the globe. To this aim, this paper makes a novel attempt to classify the brain MRI images into three classes viz. Alzheimer’s disease (AD), mild cognitive impairment (MCI) and healthy control (HC) using the volumetric information of white matter (WM), grey matter (GM) and cerebro spinal fluid (CSF). This classification has been accomplished with the help of fuzzy logic based approach followed by a majority voter classifier. Our proposition is finally tested over several brain MRI images collected from ADNI dataset. Supremacy of our proposition has strongly been established by measuring its performance parameters such as accuracy, sensitivity and specificity and subsequently been compared with many of the state-of-the-art methods. Simulation results have shown an average improvement of approximately 6.5%, 6.9% and 4% over a number of existing works in terms of accuracy, sensitivity and specificity respectively.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11(6):805–821

    Article  Google Scholar 

  2. Basaia S, Agosta F , Wagner L, Canu E, Magnani R, Santangelo M, Filippi ADN (2019) Initiative, et al, Automated classification of alzheimer’s disease and mild cognitive impairment using a single mri and deep neural networks. NeuroImage Clin 21:101645

    Article  Google Scholar 

  3. Bateman RJ, Aisen PS, De Strooper B, Fox NC, Lemere CA, Ringman JM, Salloway S, Sperling RA, Windisch M, Xiong C (2011) Autosomal-dominant alzheimer’s disease: a review and proposal for the prevention of alzheimer’s disease. Alzheimer’s Res Ther 3(1):1–13

    Google Scholar 

  4. Chandra A, Mondal S (2017) Amalgamation of iterative double automated thresholding and morphological filtering: a new proposition in the early detection of cerebral aneurysm. Multimed Tools Appl 76(22):23957–23979

    Article  Google Scholar 

  5. Chandra A, Roy S (2021) On the detection of alzheimer’s disease using support vector machine based majority voter classifier. In: 2021 8th International conference on signal processing and integrated networks (SPIN). IEEE, pp 144–149

  6. Chandra A, Roy S (2021) Difference between alzheimer’s disease and mild cognitive impairment: Ztest based study. In: proceedings of 2nd International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC 2021) IEEE

  7. Demirhan A, Nir TM, Zavaliangos-Petropulu A, Jack CR, Weiner MW , Bernstein MA, Thompson PM, Jahanshad N (2015) Feature selection improves the accuracy of classifying alzheimer disease using diffusion tensor images. IEEE

  8. Feng C, Elazab A, Yang P, Wang T, Zhou F, Hu H, Xiao X, Lei B (2019) Deep learning framework for alzheimer’s disease diagnosis via 3d-cnn and fsbi-lstm. IEEE Access 7:63605–63618

    Article  Google Scholar 

  9. Frozza RL, Lourenco MV, De Felice FG (2018) Challenges for alzheimer’s disease therapy: insights from novel mechanisms beyond memory defects. Front Neurosci 12:37

    Article  Google Scholar 

  10. Garg D, Garg NK, Kumar M (2018) Underwater image enhancement using blending of clahe and percentile methodologies. Multimed Tools Appl 77 (20):26545–26561

    Article  Google Scholar 

  11. Ghosh S, Chandra A, Mudi RK (2019) A novel fuzzy pixel intensity correlation based segmentation algorithm for early detection of alzheimer’s disease. Multimed Tools Appl 78(9):12465–12489

    Article  Google Scholar 

  12. Gupta S, Kumar M, Garg A (2019) Improved object recognition results using sift and orb feature detector. Multimed Tools Appl 78(23):34157–34171

    Article  Google Scholar 

  13. Gupta S, Thakur K, Kumar M (2020) 2D-human face recognition using sift and surf descriptors of face’s feature regions, The Visual Computer, pp 1–10

  14. Haouas I, Moussa H, Douik A (2021) Classification and identification of alzheimer disease with fuzzy logic method. In: 2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS). IEEE, pp 1–6

  15. Khvostikov A, Aderghal K, Benois-Pineau J, Krylov A, Catheline G (2018) 3d cnn-based classification using smri and md-dti images for alzheimer disease studies, arXiv:1801.05968

  16. Konar A (2006) Computational intelligence: principles, techniques and applications Springer Science & Business Media

  17. Krashenyi I, Popov A, Ramirez J, Gorriz JM (2015) Application of fuzzy logic for alzheimer’s disease diagnosis. In: 2015 Signal Processing Symposium (SPSympo). IEEE, pp 1–4

  18. Krashenyi I, Popov A, Ramirez J, Gorriz JM (2016) Fuzzy computer-aided diagnosis of alzheimer’s disease using mri and pet statistical features. In: 2016 IEEE 36th International Conference on Electronics and Nanotechnology (ELNANO). IEEE, pp 187–191

  19. Lee B, Ellahi W, Choi JY (2019) Using deep cnn with data permutation scheme for classification of alzheimer’s disease in structural magnetic resonance imaging (smri). IEICE Trans Inf Syst 102(7):1384–1395

    Article  Google Scholar 

  20. Li F, Liu M, Initiative ADN et al (2019) A hybrid convolutional and recurrent neural network for hippocampus analysis in alzheimer’s disease. J Neurosci Methods 323:108–118

    Article  Google Scholar 

  21. Lian C, Liu M, Zhang J, Shen D (2018) Hierarchical fully convolutional network for joint atrophy localization and alzheimer’s disease diagnosis using structural mri. IEEE Trans Pattern Anal Mach Intell 42(4):880–893

    Article  Google Scholar 

  22. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  23. Liu M, Cheng D, Wang K, Wang Y (2018) Multi-modality cascaded convolutional neural networks for alzheimer’s disease diagnosis. Neuroinformatics 16(3):295–308

    Article  Google Scholar 

  24. Ma X, Li Z, Jing B, Liu H, Li D, Li H, Initiative ADN et al (2016) Identify the atrophy of alzheimer’s disease, mild cognitive impairment and normal aging using morphometric mri analysis. Front Aging Neurosci 8:243

    Article  Google Scholar 

  25. Manjón JV, Coupé P (2016) Volbrain: An online mri brain volumetry system. Front Neuroinform 10:30

    Article  Google Scholar 

  26. NP KT, Varghese D (2018) A novel approach for diagnosing alzheimer’s disease using svm. In: 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, pp 895–898

  27. Patterson C (2018) The state of the art of dementia research: New frontiers, World Alzheimer Report, vol 2018

  28. Rutegård MK, Båtsman M, Axelsson J, Brynolfsson P, Brännström F, Rutegård J, Ljuslinder I, Blomqvist L, Palmqvist R, Rutegård M. et al (2019) Pet/mri and pet/ct hybrid imaging of rectal cancer–description and initial observations from the rectopet (rectal cancer trial on pet/mri/ct) study. Cancer Imaging 19(1):1–9

    Article  Google Scholar 

  29. Sadeghi N, Foster NL, Wang AY, Minoshima S, Lieberman AP, Tasdizen T (2008) Automatic classification of alzheimer’s disease vs. frontotemporal dementia: A spatial decision tree approach with fdg-pet. In: 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro,. IEEE, pp 408–411

  30. Shi J, Zheng X, Li Y, Zhang Q, Ying S (2017) Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of alzheimer’s disease. IEEE J Biomed Health Inform 22(1):173–183

    Article  Google Scholar 

  31. Telagarapu P, Mohanty B, Anandh K (2018) Analysis of alzheimer condition in t1-weighted mr images using texture features and k-nn classifier. In: 2018 international CET conference on control, communication, and computing (IC4). IEEE, pp 331–334

  32. Valverde S, Oliver A, Roura E, González-Villà S, Pareto D, Vilanova JC, Ramió-Torrentà L, Rovira À, Lladó X (2017) Automated tissue segmentation of mr brain images in the presence of white matter lesions. Med Image Anal 35:446–457

    Article  Google Scholar 

  33. Yushkevich PA, Piven J, Cody Hazlett H, Gimpel Smith R, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128

    Article  Google Scholar 

  34. Zekri F, Ghorbel H, Bouaziz R (2014) A decision support system based on fuzzy specialized rules for the alzheimer disease. In: 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, pp 490–496

Download references

Funding

This research work is funded by Ministry of Electronics & Information Technology, Govt. of India under Sir Visvesvaraya Young Faculty Research Fellowship (YFRF) scheme (Unique Awardee No. MEITY-PHD-3191).

Author information

Authors and Affiliations

  1. Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, 700106, India

    Subhabrata Roy & Abhijit Chandra

Authors
  1. Subhabrata Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhijit Chandra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhijit Chandra.

Ethics declarations

Conflicts of interest/Competing interests

The authors declare that they have no conflicts of interest/competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, S., Chandra, A. On the detection of Alzheimer’s disease using fuzzy logic based majority voter classifier. Multimed Tools Appl 81, 43145–43161 (2022). https://doi.org/10.1007/s11042-022-13184-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13184-5

Keywords

Search

Navigation