Skip to main content

Advertisement

SpringerLink
Log in

A new deep belief network-based multi-task learning for diagnosis of Alzheimer’s disease

  • S.I. : ‘Babel Fish’ for Feature-driven Machine Learning to Maximise Societal Value
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Accurate classification of Alzheimer’s disease (AD) and mild cognitive impairment (MCI), especially distinguishing the progressive MCI (pMCI) from stable MCI (sMCI), will be helpful in both reducing the risk of converting into AD and also releasing the burden on the family and even the society. In this study, a novel deep belief network (DBN) based multi-task learning algorithm is developed for the classification issue. In particular, the dropout technology and zero-masking strategy are exploited for getting over the overfitting problem and also enhancing the generalization ability and robustness of the model. Then, a new framework based on the DBN-based multi-task learning is established for accurate diagnosis of AD. After MRI preprocessing, not only the principal component analysis is utilized to reduce the feature dimension, but also multi-task feature selection approach is introduced to select the feature set related to all tasks as a result of taking the internal relevancy among multiple related tasks into consideration. Using data from the ADNI dataset, our method achieves satisfactory results in six tasks of health control (HC) vs. AD, HC vs. pMCI, HC vs. sMCI, pMCI vs. AD, sMCI vs. AD and sMCI vs. pMCI with the accuracies are 98.62%, 96.67%, 92.31%, 91.89%, 99.62% and 87.78%, respectively. Experimental results demonstrate that the DBN-based MTL algorithm developed in this study is an effective, superior and practical method of AD diagnosis.

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. Suk H, Lee S, Shen D (2014) Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 101:569–582

    Google Scholar 

  2. Suk H, Shen D (2013) Deep learning-based feature representation for AD/MCI classification. Med Image Comput Computer-Assisted Interv. pp 583–590

  3. Petersen R (2002) Mild cognitive impairment: transition from aging to Alzheimer's disease. Neurobiol Aging 21:141–151

    Google Scholar 

  4. Cano S, Posner H, Moline M (2010) The ADAS-cog in Alzheimer's disease clinical trials: psychometric evaluation of the sum and its parts. J Neurol Neurosurg Psychiatry 81(12):1363–1368

    Google Scholar 

  5. Vemuri P, Jones D, C Jr (2012) Resting state functional MRI in Alzheimer's disease. Alzheimers Res Therap 4(1):1–9

    Google Scholar 

  6. Rye P, Booij B, Grave G (2011) A novel blood test for the early detection of Alzheimer's disease. J Alzheimer's Dis Jad Nol 23(1):121–129

    Google Scholar 

  7. OBryant S, Edwards M, Johnson L (2016) A blood screening test for Alzheimer's disease. Alzheimer's Dementia 3:83–90

    Google Scholar 

  8. Weeraratna A, Kalehua A, Deleon I (2007) Alterations in immunological and neurological gene expression patterns in Alzheimer's disease tissues. Exp Cell Res 313(3):450–461

    Google Scholar 

  9. Spinnler H, Sala S (1988) The role of clinical neuropsychology in the neurological diagnosis of Alzheimer's disease. J Neurol 235(5):258–271

    Google Scholar 

  10. Chauhan G, Adams H (2015) Association of Alzheimer's disease GWAS loci with MRI markers of brain aging. Neurobiol Aging 36(4):7–16

    Google Scholar 

  11. Yang W, Lui R, Gao J (2011) Independent component analysis-based classification of Alzheimer's disease MRI data. J Alzheimer's Dis 24(4):775–783

    Google Scholar 

  12. Ye J, Wu T, Li J (2011) Machine learning approaches for the neuroimaging study of Alzheimer's disease. Computer 44(4):99–101

    Google Scholar 

  13. Salvatore C, Cerasa A, Battista P (2015) Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach. Front Neurosci 9:1–13

    Google Scholar 

  14. Escudero J, Zajicek J, Ifeachor E (2011) Machine learning classification of MRI features of Alzheimer’s disease and mild cognitive impairment subjects to reduce the sample size in clinical trials. In: International conference of the IEEE engineering in medicine and biology society. pp 7957–7960

  15. Sarraf S, Tofighi G (2016) Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data. IEEE Future Technol Conf. https://doi.org/10.1109/FTC.2016.7821697

    Article  Google Scholar 

  16. Sarraf S, Tofighi G (2016) Classification of Alzheimer’s disease using fMRI data and deep learning convolutional neural networks. arXiv:1603.08631

  17. Ortiz A, Munilla J, Gorriz J (2016) Ensembles of deep learning architectures for the early diagnosis of the Alzheimer's disease. Int J Neural Syst 26(7):1650025

    Google Scholar 

  18. Hu C, Ju R, Shen Y (2016) Clinical decision support for Alzheimer’s disease based on deep learning and brain network. In: IEEE international conference on communications. pp 1–6

  19. Bhatkoti P, Paul M (2016) Early diagnosis of Alzheimer's disease: a multi-class deep learning framework with modified k-sparse autoencoder classification. Int Conf Image Vis Comput. https://doi.org/10.1109/IVCNZ.2016.7804459

    Article  Google Scholar 

  20. Liaqat S, Dashtipour K, Zahid A, Assaleh K, Arshad K, Ramzan N (2020) Detection of atrial fibrillation using a machine learning approach. Information 11(12):549

    Google Scholar 

  21. Liu J, Li M, Luo Y, Yang S, Li W, Bi Y (2021) Alzheimer's disease detection using depthwise separable convolutional neural networks. Comput Methods Prog Biomed 203:106032

    Google Scholar 

  22. Tian J, Smith G, Guo H, Liu B, Pan Z, Wang Z, Xiong S, Fang R (2021) Modular machine learning for Alzheimer's disease classification from retinal vasculature. Sci Rep 11(1):1–11

    Google Scholar 

  23. Farooq A, Anwar S, Awais M (2018) A deep CNN based multi-class classification of Alzheimer’s disease using MRI. In: IEEE International conference on imaging systems and techniques. pp 1–6

  24. Thung K, Yap P, Shen D (2017) Multi-stage diagnosis of Alzheimer's disease with incomplete multimodal data via multi-task deep learning. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support 10553:160–168

    Google Scholar 

  25. Islam J, Zhang Y (2017) A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. In: International conference on brain informatics. pp 213–222

  26. Liu S, Liu S, Cai W (2015) Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Trans Biomed Eng 62(4):1132–1140

    Google Scholar 

  27. Zhang D, Shen D (2012) Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. Neuroimage 59(2):895–907

    MathSciNet  Google Scholar 

  28. Folstein M, Folstein S, Mchugh P (1975) Mini-mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198

    Google Scholar 

  29. Rosen W, Mohs R, Davis K (1984) A new rating scale for Alzheimer's disease. Am J Psychiatr 141(11):1356–1364

    Google Scholar 

  30. Jack C, Bernstein M, Fox N (2008) The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 27(4):685–691

    Google Scholar 

  31. Smith S (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155

    Google Scholar 

  32. Moeller S, Konova A, Parvaz M (2014) Functional, structural, and emotional correlates of impaired insight in cocaine addiction. JAMA Psychiatr 71(1):61–70

    Google Scholar 

  33. Goto M, Abe O, Miyati T (2012) Influence of signal intensity non-uniformity on brain volumetry using an atlas-based method. Korean J Radiol 13(4):391–402

    Google Scholar 

  34. Nielsen F, Hansen L (2002) Automatic anatomical labeling of Talairach coordinates and generation of volumes of interest via the BrainMap database. Neuroimage 16(2):1–2

    Google Scholar 

  35. Maldjian J, Laurienti P, Burdette J, Kraft R (2003) An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19(3):1233–1239

    Google Scholar 

  36. Maldjian J, Laurienti P, Burdette J (2004) Precentral gyrus discrepancy in electronic versions of the Talairach atlas. Neuroimage 21(1):450–455

    Google Scholar 

  37. Smith T (1991) Principal component analysis: an introduction. Anal Proc 28:150–151

    Google Scholar 

  38. Destefanis G, Barge M, Brugiapaglia A (2000) The use of principal component analysis (PCA) to characterize beef. Meat Sci 56(3):255–259

    Google Scholar 

  39. Fan Y, Kaufer D, Shen D (2010) Joint estimation of multiple clinical variables of neurological diseases from imaging patterns. In: IEEE international symposium on biomedical imaging: from nano to macro. pp 852–855

  40. Stonnington C, Chu C, Kloppel S (2010) Predicting clinical scores from magnetic resonance scans in Alzheimer's disease. Neuroimage 51(4):1405–1413

    Google Scholar 

  41. Jie B, Zhang D, Cheng B (2015) Manifold regularized multitask feature learning for multimodality disease classification. Hum Brain Mapp 36(2):489–507

    Google Scholar 

  42. Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc 68(1):49–67

    MathSciNet  MATH  Google Scholar 

  43. Hinton G, Salakhutdinov R (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507

    MathSciNet  MATH  Google Scholar 

  44. Hinton G, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    MathSciNet  MATH  Google Scholar 

  45. Lü Q, Dou Y, Niu X (2014) Remote sensing image classification based on DBN model. J Comput Res Dev 51(9):1911–1918

    Google Scholar 

  46. Dahl G, Yu D, Deng L (2011) Large vocabulary continuous speech recognition with context-dependent DBN-HMMS. In: International conference on acoustics, speech and signal processing. pp 4688–4691

  47. Chen J, Zhou J, Ye J (2011) Integrating low-rank and group-sparse structures for robust multi-task learning. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. pp 21–24

  48. Srivastava N, Hinton G, Krizhevsky A (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  49. Vincent P, Larochelle H, Lajoie I (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12):3371–3408

    MathSciNet  MATH  Google Scholar 

  50. Vincent P, Larochelle H, Bengio Y (2008) Extracting and composing robust features with denoising autoencoders. Int Conf Mach Learn. pp 1096–1103

  51. Li F, Tran L, Thung K (2015) A robust deep model for improved classification of AD/MCI patients. IEEE J Biomed Health Inf 19(5):1610–1616

    Google Scholar 

  52. Liu S, Liu S, Cai W (2014) Early diagnosis of Alzheimer’s disease with deep learning. In: IEEE international symposium on biomedical imaging. pp 1015–1018

  53. Liu M, Zhang D, Shen D (2014) Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis. Hum Brain Mapp 35(4):1305–1319

    Google Scholar 

  54. Zeng N, Qiu H, Wang Z, Liu W (2018) A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer's disease. Neurocomputing 320:195–202

    Google Scholar 

  55. Trambaiolli L, Lorena A, Fraga F (2011) Improving Alzheimer's disease diagnosis with machine learning techniques. Clin Eeg Neurosci 42(3):160–165

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Instrumental and Electrical Engineering, Xiamen University, Fujian, 361102, China

    Nianyin Zeng & Han Li

  2. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK

    Yonghong Peng

Authors
  1. Nianyin Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Han Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yonghong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yonghong Peng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

This work was supported in part by International Science and Technology Cooperation Project of Fujian Province of China under Grant 2019I0003, in part by the UK-China Industry Academia Partnership Programme under Grant UK-CIAPP-276, in part by the Korea Foundation for Advanced Studies, in part by the Fundamental Research Funds for the Central Universities under Grant 20720190009, in part by The Open Fund of Provincial Key Laboratory of Eco-Industrial Green Technology-Wuyi University, in part by The Open Fund of Engineering Research Center of Big Data Application in Private Health Medicine of Fujian Province University under Grant KF2020002, and in part by Fujian Key Laboratory of Automotive Electronics and Electric Drive (Fujian University of Technology) Grant KF-X19002. Data used in this work were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zeng, N., Li, H. & Peng, Y. A new deep belief network-based multi-task learning for diagnosis of Alzheimer’s disease. Neural Comput & Applic 35, 11599–11610 (2023). https://doi.org/10.1007/s00521-021-06149-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06149-6

Keywords

Search

Navigation