Advertisement

A Study on Various Deep Learning Algorithms to Diagnose Alzheimer’s Disease

  • M. Deepika Nair
  • M. S. Sinta
  • M. Vidya
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Alzheimer’s disease (AD) is one of the most frequent types of dementia, which is deterioration in mental ability severe enough to interfere with daily life and gradually affect the human’s brain, its capability to learn, think and communicate. The symptoms of AD develop over time and become a major brain disease over the course of several years. To bring out patterns from the brain neuroimaging data, different statistical and machine learning approaches have been used to find the Alzheimer’s disease present in older adults at clinical as well as research applications; however, differentiating the phases of the Alzheimer’s and healthy brain data has been difficult due to the similarity in brain atrophy patterns and image intensities. Recently, number of deep learning methods has been expeditiously developing into numerous areas, which consist of medical image analysis. This survey gives out the idea of deep learning-based methods which used to differentiate between Alzheimer’s Magnetic Resonance Imaging (MRI) and functional MRI from the normal healthy control data.

Keywords

Deep learning Alzheimer’s disease MRI fMRI 

References

  1. 1.
    Bishop C (2006) Pattern recognition and machine learning. Springer-Verlag, New YorkzbMATHGoogle Scholar
  2. 2.
    American Society of Health-System Pharmacists (2001) Alzheimer’s disease education and referral center. Am J Health-Syst Pharm 58(9):826Google Scholar
  3. 3.
    Vieira S, Pinaya WHL, Mechelli A, Serif L (2017) Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci Biobehav Rev 74(Part A)Google Scholar
  4. 4.
    Shin HC, Orton MR, Collins DJ, Doran SJ, Leach MO (2013) Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4d patient data. IEEE Trans Pattern Anal Mach Intell 35:19301943Google Scholar
  5. 5.
    JhaD, Kwon G-R (2017) Alzheimer’s disease detection using sparse autoencoder, scale conjugate gradient and softmax output layer with fine tuning. Int J Mach Learn Comput 7(1)CrossRefGoogle Scholar
  6. 6.
    Bhatkoti P, Paul M (2016) Early diagnosis of alzheimer’s disease: a multi-class deep learning framework with modified k-sparse autoencoder classification. IEEE 2016Google Scholar
  7. 7.
    Arel I, Rose DC, Karnowski TP (2010) Deep machine learning a new frontier in artificial intelligence research [research frontier]. Comput Intell Mag IEEE 5(4):1318CrossRefGoogle Scholar
  8. 8.
    Jia SE, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell CT (2014) Convolutional architecture for fast feature embedding. In: Proceedings of the ACM international conference on multimedia, pp 675678, ACMGoogle Scholar
  9. 9.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient based learning applied to document recognition. Proc IEEE 86(11):22782324CrossRefGoogle Scholar
  10. 10.
    Sarraf S, Tofighi G (2016) Classification of alzheimer’s disease using fMRI Data and deep learning convolutional neural networksGoogle Scholar
  11. 11.
    Ciprian D, Billones Jr, Louville OJ, Demetria D, Earl D, Hostallero D, Prospero, Naval Jr. C (2016) A convolutional neural network for the detection of alzheimer’s disease and mild cognitive impairment. IEEE 2016Google Scholar
  12. 12.
    Glozman T, Liba O (2016) Cues: deep learning for alzheimer’s disease classification. CS331B project final report, 2016Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science EngineeringVidya Academy of Science and TechnologyThrissurIndia

Personalised recommendations