3D MRI Classification Using KNN and Deep Neural Network for Alzheimer’s Disease Diagnosis

  • El Mehdi BenyoussefEmail author
  • Abdeltif Elbyed
  • Hind El Hadiri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 914)


Alzheimer’s disease (AD) is known as one of the most common neurodegenerative diseases which causes permanent damage to the brain cells related to memory and thinking skills. Research in this field aims to identify the most specific structures directly related to the changes in AD. MRI is one of the main imaging modalities which plays a huge role in AD diagnosis. Images produced in MRI helps us get information on anatomical structures in the brain and can also be used for clinical diagnosis of AD stages. In the recent years, deep learning has gained huge fame in solving complex problems from lots of fields, medical image analysis is one of them. This work proposes a K-Nearest Neighbor and a Deep Neural Network combined model for the early diagnosis of Alzheimer’s disease and its stages using 3D magnetic resonance imaging (MRI) scans.


Magnetic resonance imaging Machine learning Brain Data modeling Alzheimer’s disease Image classification Neural networks 


  1. 1.
    Barnes, J., Scahill, R.I., Boyes, R.G., Frost, C., Lewis, E.B., Rossor, C.L., Rossor, M.N., Fox, N.C.: Differentiating ad from aging using semiautomated measurement of hippocampal atrophy rates. Neuroimage 23(2), 574–581 (2004)CrossRefGoogle Scholar
  2. 2.
    Benyoussef, E.M., Elbyed, A., El Hadiri, H.: Data mining approaches for alzheimer’s disease diagnosis. In: International Symposium on Ubiquitous Networking, pp. 619–631. Springer (2017)Google Scholar
  3. 3.
    Fox, N.C., Schott, J.M.: Imaging cerebral atrophy: normal ageing to Alzheimer’s disease. The Lancet 363(9406), 392–394 (2004)CrossRefGoogle Scholar
  4. 4.
    Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  5. 5.
    Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv preprint arXiv:1502.02506 (2015)
  6. 6.
    Tufail, A.B., Abidi, A., Siddiqui, A.M., Younis, M.S.: Automatic classification of initial categories of Alzheimer’s disease from structural MRI phase images: a comparison of PSVM, KNN and ANN methods. In: Proceedings of World Academy of Science, Engineering and Technology, p. 1731. World Academy of Science, Engineering and Technology (WASET) (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • El Mehdi Benyoussef
    • 1
    Email author
  • Abdeltif Elbyed
    • 1
  • Hind El Hadiri
    • 2
  1. 1.LIMSAD, Faculty of ScienceHassan II UniversityCasablancaMorocco
  2. 2.Geriatric Service, Emile Roux HospitalParisFrance

Personalised recommendations