Ensemble of 3D Densely Connected Convolutional Network for Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease

  • Shuqiang Wang
  • Hongfei Wang
  • Albert C. Cheung
  • Yanyan Shen
  • Min GanEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1098)


Automatic diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) from 3D brain magnetic resonance (MR) images play an important role in the early treatment of dementia disease. Deep learning architectures can extract potential features of dementia disease and capture brain anatomical changes from MRI scans. Given the high dimension and complex features of the 3D medical images, computer-aided diagnosis is still confronted with challenges. Firstly, compared with the number of learnable parameters, the number of training samples is very limited, which can cause overfitting problems. Secondly, the deepening of the network layer makes gradient information gradually weaken and even disappears in the process of transmission, resulting in mode collapse. This chapter proposed an ensemble of 3D densely connected convolutional networks for AD and MCI diagnosis from 3D MRIs. Dense connections were introduced to maximize the information flow, where each layer connects with all subsequent layers directly. Bottleneck layers and transition layers are also employed to reduce parameters and lead to more compact models. Then the probability-based fusion method was employed to combine 3D-DenseNets with different architectures. Extensive experiments were conducted to analyze the performance of 3D-DenseNet with different hyperparameters and architectures. Superior performance of the proposed model was demonstrated on ADNI dataset.


  1. 1.
    Alzheimer’s Association et al., 2017 Alzheimer’s disease facts and figures. Alzheimer’s Dement. 13(4), 325–373 (2017)Google Scholar
  2. 2.
    S. Li, O. Okonkwo, M. Albert, M.-C. Wang, Variation in variables that predict progression from MCI to AD dementia over duration of follow-up. Am. J. Alzheimer’s Dis. 2(1), 12–28 (2013)Google Scholar
  3. 3.
    R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, M.-O. Habert, M. Chupin, H. Benali, O. Colliot, A.D.N. Initiative et al., Automatic classification of patients with alzheimer’s disease from structural MRI: a comparison of ten methods using the adni database. Neuroimage 56(2), 766–781 (2011)CrossRefGoogle Scholar
  4. 4.
    F. Falahati, E. Westman, A. Simmons, Multivariate data analysis and machine learning in alzheimer’s disease with a focus on structural magnetic resonance imaging. J. Alzheimer’s Dis. 41(3), 685–708 (2014)CrossRefGoogle Scholar
  5. 5.
    E. Moradi, A. Pepe, C. Gaser, H. Huttunen, J. Tohka, A.D.N. Initiative et al., Machine learning framework for early MRI-based alzheimer’s conversion prediction in mci subjects. Neuroimage 104, 398–412 (2015)CrossRefGoogle Scholar
  6. 6.
    J.-Z. Cheng, D. Ni, Y.-H. Chou, J. Qin, C.-M. Tiu, Y.-C. Chang, C.-S. Huang, D. Shen, C.-M. Chen, Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans. Sci. Rep. 6, 24454 (2016)CrossRefGoogle Scholar
  7. 7.
    S.M. Plis, D.R. Hjelm, R. Salakhutdinov, E.A. Allen, H.J. Bockholt, J.D. Long, H.J. Johnson, J.S. Paulsen, J.A. Turner, V.D. Calhoun, Deep learning for neuroimaging: a validation study. Front. Neurosci. 8, 229 (2014)CrossRefGoogle Scholar
  8. 8.
    F.C. Ghesu, B. Georgescu, T. Mansi, D. Neumann, J. Hornegger, D. Comaniciu, An artificial agent for anatomical landmark detection in medical images, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Berlin, 2016), pp. 229–237Google Scholar
  9. 9.
    W. Shen, M. Zhou, F. Yang, C. Yang, J. Tian, Multi-scale convolutional neural networks for lung nodule classification, in International Conference on Information Processing in Medical Imaging (Springer, Berlin, 2015), pp. 588–599Google Scholar
  10. 10.
    S. Wang, Y. Shen, C. Shi, P. Yin, Z. Wang, P.W.-H. Cheung, J.P.Y. Cheung, K.D.-K. Luk, Y. Hu, Skeletal maturity recognition using a fully automated system with convolutional neural networks, IEEE Access 6, 29979–29993 (2018)Google Scholar
  11. 11.
    S.L. Risacher, A.J. Saykin, J.D. Wes, L. Shen, H.A. Firpi, B.C. McDonald, Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr. Alzheimer Res. 6(4), 347–361 (2009)CrossRefGoogle Scholar
  12. 12.
    W. Cai, S. Liu, L. Wen, S. Eberl, M. J. Fulham, D. Feng, 3D neurological image retrieval with localized pathology-centric CMRGlc patterns, in 2010 17th IEEE International Conference on Image Processing (ICIP) (IEEE, Piscataway, 2010), pp. 3201–3204Google Scholar
  13. 13.
    S. Liu, Y. Song, W. Cai, S. Pujol, R. Kikinis, X. Wang, D. Feng, Multifold Bayesian kernelization in Alzheimer’s diagnosis, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Berlin, 2013), pp. 303–310Google Scholar
  14. 14.
    D. Zhang, Y. Wang, L. Zhou, H. Yuan, D. Shen, A.D.N. Initiative et al., Multimodal classification of Alzheimer’s disease and Mild Cognitive Impairment. Neuroimage 55(3), 856–867 (2011)CrossRefGoogle Scholar
  15. 15.
    F. Zhang, Y. Song, S. Liu, S. Pujol, R. Kikinis, M. Fulham, D. Feng, W. Cai, Semantic association for neuroimaging classification of PET images. J. Nucl. Med. 55(supplement 1), 2029 (2014)Google Scholar
  16. 16.
    S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M.J. Fulham et al., Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015)CrossRefGoogle Scholar
  17. 17.
    F. Li, L. Tran, K.-H. Thung, S. Ji, D. Shen, J. Li, A robust deep model for improved classification of AD/MCI patients. IEEE J. Biomed. Health Inform. 19(5), 1610–1616 (2015)CrossRefGoogle Scholar
  18. 18.
    C.D. Billones, O.J. L.D. Demetria, D.E.D. Hostallero, P.C. Naval, Demnet: a convolutional neural network for the detection of Alzheimer’s Disease and Mild Cognitive Impairment, in Proceedings of the 2016 IEEE Region 10 Conference (TENCON) (IEEE, Piscataway, 2016), pp. 3724–3727Google Scholar
  19. 19.
    E. Hosseini-Asl, R. Keynton, A. El-Baz, Alzheimer’s disease diagnostics by adaptation of 3D convolutional network, in 2016 IEEE International Conference on Image Processing (ICIP). (IEEE, Piscataway, 2016), pp. 126–130Google Scholar
  20. 20.
    A. Payan, G. Montana, Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks (2015), arXiv:1502.02506
  21. 21.
    D. Cheng, M. Liu, J. Fu, Y. Wang, Classification of MR brain images by combination of multi-CNNs for ad diagnosis, in Ninth International Conference on Digital Image Processing (ICDIP 2017), vol. 10420 (International Society for Optics and Photonics, Bellingham, 2017), p. 1042042Google Scholar
  22. 22.
    C.R. Jack, M.A. Bernstein, N.C. Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P.J. Britson, J.L. Whitwell, C. Ward et al., The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2008)CrossRefGoogle Scholar
  23. 23.
    M.W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckmann, M. Jenkinson, S.M. Smith, Bayesian analysis of neuroimaging data in FSL. Neuroimage 45(1), S173–S186 (2009)CrossRefGoogle Scholar
  24. 24.
    M. Jenkinson, P. Bannister, M. Brady, S. Smith, Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2), 825–841 (2002)CrossRefGoogle Scholar
  25. 25.
    G. Huang, Z. Liu, K.Q. Weinberger, L. van der Maaten, Densely connected convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, Issue 2, (2017), p. 3Google Scholar
  26. 26.
    S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, in International Conference on Machine Learning (2015), pp. 448–456Google Scholar
  27. 27.
    C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2818–2826Google Scholar
  28. 28.
    K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition (2016), pp. 770–778Google Scholar
  29. 29.
    G. Wen, Z. Hou, H. Li, D. Li, L. Jiang, E. Xun, Ensemble of deep neural networks with probability-based fusion for facial expression recognition. Cogn. Comput. 9(5), 597–610 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shuqiang Wang
    • 1
  • Hongfei Wang
    • 1
  • Albert C. Cheung
    • 2
  • Yanyan Shen
    • 1
  • Min Gan
    • 3
    Email author
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.Hong Kong University of Science and TechnologyHong Kong SARChina
  3. 3.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina

Personalised recommendations