Skip to main content
SpringerLink
Log in

A Novel Deep Convolutional Neural Network Model for Alzheimer’s Disease Classification Using Brain MRI

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is a slowly progressive degenerative neurological disease characterized by severe degeneration of cortical neurons. Detecting Alzheimer’s is a difficult and time consuming task. Therefore, developing an automated analysis system that is fast, costs less, and is more reliable is essential to help clinicians in the early diagnosis. In this paper, we proposed a deep convolutional neural network (IRDNet) to classify Alzheimer’s disease from normal controls (NC) using brain magnetic resonance (MR) images. The proposed IRDNet is based on residual network with dense block to obtain a higher performance over the original residual network and is constructed by using three parallel levels with receptive fields of different sizes to capture global and local features of the inputs. Our model is evaluated and trained on brain MRI images acquired from the Open Access Series of Imaging Studies database (OASIS). The experimental results reveal that our IRDNet achieved an average accuracy of 98.53% after training it for 3.127 seconds for two-class classification (AD vs. normal), which represents a promising classification performance.

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.

Similar content being viewed by others

REFERENCES

  1. McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., and Stadlan, E.M, Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease, Neurology, 1984, vol. 34, no. 7, p. 939.  https://doi.org/10.1212/WNL.34.7.939

    Article  Google Scholar 

  2. Ewers, M., Sperling, R., Klunk, W., Weiner, M., and Hampel, H., Neuro-imaging markers for the prediction and early diagnosis of Alzheimer’s disease dementiam, Trends Neurosci., 2011, vol. 34, no. 8, pp. 430–442.  https://doi.org/10.1016/j.tins.2011.05.005

    Article  Google Scholar 

  3. Duchi, J., Hazan, E., and Singer, Y., Adaptive subgradient methods for online learning and stochastic optimization, J. Mach. Learn. Res., 2011, vol. 12, pp. 2121–2159.

    MathSciNet  MATH  Google Scholar 

  4. Liu, M., Li, F., Yan, H., Wang, K., Ma, Y., Alzheimer’s Disease Neuroimaging Initiative, Shen, L., and Xu, M., A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease, NeuroImage, 2020, vol. 208, p. 116459.  https://doi.org/10.1016/j.neuroimage.2019.116459

    Article  Google Scholar 

  5. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., Gradient-based learning applied to document recognition, Proc. IEEE, 1998, vol. 86, no. 11, pp. 2278–2324.  https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  6. Krizhevsky, A., Sutskever, I., and Hinton, G.E., Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25, Pereira, F., Burges, C.J., Bottou, L., and Weinberger, K.Q., Eds., Curran Associates, 2012.

  7. Choi, B., Madusanka, N., Choi, H., So, J., Kim, C., Park, H., Bhattacharjee, S., and Prakash, D., Convolutional neural network-based MR image analysis for Alzheimer’s disease classification, Curr. Med. Imaging, 2020, vol. 16, no. 1, pp. 27–35.  https://doi.org/10.2174/1573405615666191021123854

    Article  Google Scholar 

  8. Simonyan, K. and Zisserman, A., Very deep convolutional networks for large-scale image recognition, 2014. arXiv:1409.1556v6 [cs.CV]

  9. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A., Going deeper with convolutions, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, 2015, IEEE, 2015.  https://doi.org/10.1109/CVPR.2015.7298594

  10. Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E., Squeeze-and-excitation networks, IEEE Trans. Pattern Anal. Mach. Intell., 2020, vol. 42, no. 8, pp. 2011–2023.  https://doi.org/10.1109/TPAMI.2019.2913372

    Article  Google Scholar 

  11. Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K. Dally, W. J., and Keutzer, K., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size, 2016. arXiv:1602.07360 [cs.CV]

  12. Huang, G., Liu, Z., Van der Maaten, L., and Weinberger, K.Q., Densely connected convolutional networks, IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu, Hawaii, 2017, IEEE, 2017, pp. 2261–2269.  https://doi.org/10.1109/CVPR.2017.243

  13. He, K., Zhang, X., Ren, S., and Sun, J., Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification, IEEE Int. Conf. on Computer Vision (ICCV), Santiago, 2015, IEEE, 2015, pp. 1026–1034.  https://doi.org/10.1109/ICCV.2015.123

  14. Marcus, D., Wang, T., Parker, J., Csernansky, J., Morris, J., and Buckner, R.L., Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults, J. Cognit. Neurosci., 2007, vol. 19, no. 9, pp. 1498–1507.  https://doi.org/10.1162/jocn.2007.19.9.1498

    Article  Google Scholar 

  15. Nair, V. and Hinton, G.E., Rectified linear units improve restricted Boltzmann machines, Proc. 27th Int. Conf. on Machine Learning, Haifa, Israel, 2010, Fürnkranz, J. and Joachims, T., Eds., Madison, Wis.: Omnipress, 2010, pp. 807–814.

  16. Ioffe, S., and Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proc. 32nd Int. Conf. on Machine Learning, Lille, 2015, Bach, F. and Blei, D., Eds., JLMR.org, 2015, pp. 448–456.

  17. David, E.R., Geoffrey, E.H., and Ronald, J.W., Learning internal representations by error propagation, Parallel Distributed Processing, Rumelhart, D.E., McClelland, J.L., and PDP Research Group, Eds., Cambridge, Mass.: MIT Press, 1986, p. 318.

  18. Kingma, D. and Ba, J., Adam: A method for stochastic optimization, 3rd Int. Conf. for Learning Representations, San Diego, 2015. arXiv:1412.6980 [cs.LG]

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, Faculty of Science and Technology of Fez, P.O. Box 2202, Fez, Morocco

    Chaimae Ouchicha & Ouafae Ammor

  2. Department of informatics, Faculty of Sciences Dhar Mehraz, P.O. Box 1796, Atlas Fez, Morocco

    Mohammed Meknassi

Authors
  1. Chaimae Ouchicha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ouafae Ammor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Meknassi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chaimae Ouchicha.

Ethics declarations

COMPLIANCE WITH ETHICAL STANDARDS

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaimae Ouchicha, Ammor, O. & Meknassi, M. A Novel Deep Convolutional Neural Network Model for Alzheimer’s Disease Classification Using Brain MRI. Aut. Control Comp. Sci. 56, 261–271 (2022). https://doi.org/10.3103/S0146411622030063

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411622030063

Keywords:

Search

Navigation