Skip to main content
SpringerLink
Log in

Deep Learning in Smart Health: Methodologies, Applications, Challenges

  • Chapter
  • First Online:
Book cover Connected Health in Smart Cities

Abstract

The advent of artificial intelligence methodologies pave the way towards smarter healthcare by exploiting new concepts such as deep learning. This chapter presents an overview of deep learning techniques that are applied to smart healthcare. Deep learning techniques are frequently applied to smart health to enable AI-based recent technological development to healthcare. Furthermore, the chapter also introduces challenges and opportunities in deep learning particularly in the healthcare domain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A.H. Abdulnabi, G. Wang, J. Lu, K. Jia, Multi-task CNN model for attribute prediction. IEEE Trans. Multimedia 17(11), 1949–1959 (2015)

    Article  Google Scholar 

  2. D.H. Ackley, G.E. Hinton, T.J. Sejnowski, A learning algorithm for Boltzmann machines. Cogn. Sci. 9(1), 147–169 (1985)

    Article  Google Scholar 

  3. I.N. Aizenberg, N.N. Aizenberg, G.A. Krivosheev, Multi-valued and universal binary neurons: learning algorithms, application to image processing and recognition. Lecture Notes Comput. Sci. 1715(4), 306–316 (1999)

    Article  Google Scholar 

  4. G. Alain, Y. Bengio, S. Rifai, Regularized auto-encoders estimate local statistics, in Proc. CoRR (2012), pp. 1–17

    Google Scholar 

  5. B. Alipanahi, A. Delong, M.T. Weirauch, B.J. Frey, Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33(8), 831 (2015)

    Article  Google Scholar 

  6. E. Alpaydin, Introduction to Machine Learning (MIT Press, Cambridge, 2014)

    MATH  Google Scholar 

  7. M.M. Baig, H. Gholamhosseini, Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37(2), 9898 (2013)

    Google Scholar 

  8. Y. Bar, I. Diamant, L. Wolf, H. Greenspan, Deep learning with non-medical training used for chest pathology identification, in SPIE Medical Imaging, vol. 9414 (2015), pp. 94140V-1–94140V-7

    Google Scholar 

  9. Y. Chen, Y. Li, R. Narayan, A. Subramanian, X. Xie, Gene expression inference with deep learning. Bioinformatics 32(12), 1832–1839 (2016)

    Article  Google Scholar 

  10. P. Danaee, R. Ghaeini, D.A. Hendrix, A deep learning approach for cancer detection and relevant gene identification, in Pacific Symposium on Biocomputing (World Scientific, Singapore, 2017), pp. 219–229

    Google Scholar 

  11. A. De Carvalho, M.C. Fairhurst, D.L. Bisset, An integrated Boolean neural network for pattern classification. Pattern Recogn. Lett. 15(8), 807–813 (1994)

    Article  Google Scholar 

  12. L. Deng, O. Abdelhamid, D. Yu, A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2013), pp. 6669–6673

    Google Scholar 

  13. R. Fakoor, F. Ladhak, A. Nazi, M. Huber, Using deep learning to enhance cancer diagnosis and classification, in Proceedings of the International Conference on Machine Learning, vol. 28 (2013)

    Google Scholar 

  14. R. Fan, F.-L. Zhang, M. Zhang, R.R. Martin, Robust tracking-by-detection using a selection and completion mechanism. Comput. Visual Media 3(3), 285–294 (2017)

    Article  Google Scholar 

  15. M. Finger, M. Razaghi, Conceptualizing “smart cities”. Informatik-Spektrum 40(1), 6–13 (2017)

    Article  Google Scholar 

  16. M. Frandes, B. Timar, D. Lungeanu, A risk based neural network approach for predictive modeling of blood glucose dynamics. Stud. Health Technol. Inform. 228, 577–581 (2016)

    Google Scholar 

  17. K. Fukushima, Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)

    Article  MATH  Google Scholar 

  18. V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)

    Article  Google Scholar 

  19. G.E. Hinton, Learning multiple layers of representation. Trends Cogn. Sci. 11(10), 428 (2007)

    Article  Google Scholar 

  20. G.E. Hinton, P. Dayan, B.J. Frey, R.M. Neal, The “wake-sleep” algorithm for unsupervised neural networks. Science 268(5214), 1158 (1995)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  22. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  23. G. Huang, H. Lee, E. Learnedmiller, Learning hierarchical representations for face verification with convolutional deep belief networks, in 2012 IEEE Conference on Computer Vision and Pattern Recognition (2012), pp. 2518–2525

    Google Scholar 

  24. A.G. Ivakhnenko, V.G. Lapa, Cybernetic predicting devices. Transdex (1966)

    Google Scholar 

  25. M.I. Jordan, T.M. Mitchell, Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  26. K. Kamnitsas, C. Ledig, V.F.J. Newcombe, J.P. Simpson, A.D. Kane, D.K. Menon, D. Rueckert, B. Glocker, Efficient Multi-Scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    Article  Google Scholar 

  27. Y. Kim, J.W. Chong, K.H. Chon, J. Kim, Wavelet-based AR–SVM for health monitoring of smart structures. Smart Mater. Struct. 22(1), 015003 (2012)

    Article  Google Scholar 

  28. H. Larochelle, Y. Bengio, Classification using discriminative restricted Boltzmann machines, in International Conference (2008), pp. 536–543

    Google Scholar 

  29. Y. Lecun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (2014)

    Article  Google Scholar 

  30. T. Lee, S. Yoon, Boosted categorical restricted Boltzmann machine for computational prediction of splice junctions, in International Conference on Machine Learning (2015), pp. 2483–2492

    Google Scholar 

  31. M.K.K. Leung, H.Y. Xiong, L.J. Lee, B.J. Frey, Deep learning of the tissue-regulated splicing code. Bioinformatics 30(12), i121–i129 (2014)

    Article  Google Scholar 

  32. J.M. Levy, Contemporary Urban Planning (Taylor & Francis, London, 2016)

    Book  Google Scholar 

  33. C. Li, X. Wang, W. Liu, L.J. Latecki, DeepMitosis: mitosis detection via deep detection, verification and segmentation networks. Med. Image Anal. (2018)

    Google Scholar 

  34. S. Liu, H. Zheng, Y. Feng, W. Li, Prostate cancer diagnosis using deep learning with 3D multiparametric MRI, in Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134 (International Society for Optics and Photonics, Bellingham, 2017), page 1013428

    Google Scholar 

  35. F.S. Lu, S. Hou, K. Baltrusaitis, M. Shah, J. Leskovec, R. Sosic, J. Hawkins, J. Brownstein, G. Conidi, J. Gunn, J. Gray, A. Zink, M. Santillana, Accurate influenza monitoring and forecasting using novel internet data streams: a case study in the Boston Metropolis. JMIR Public Health Surveill. 4(1), e4 (2018)

    Article  Google Scholar 

  36. J. Lyons, A. Dehzangi, R. Heffernan, A. Sharma, K. Paliwal, A. Sattar, Y. Zhou, Y. Yang, Predicting backbone cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. J. Comput. Chem. 35(28), 2040–2046 (2014)

    Article  Google Scholar 

  37. R. Miotto, L. Li, B.A. Kidd, J.T. Dudley, Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6(1), 26094 (2016)

    Google Scholar 

  38. M. Mohri, A. Rostamizadeh, A. Talwalkar, Foundations of Machine Learning (MIT Press, Cambridge, 2012)

    MATH  Google Scholar 

  39. A. Obinikpo, B. Kantarci, Big sensed data meets deep learning for smarter health care in smart cities. J. Sens. Actuator Netw. 6(4), 22 (2017)

    Article  Google Scholar 

  40. T. Pham, T. Tran, D. Phung, S. Venkatesh, DeepCare: a deep dynamic memory model for predictive medicine, in Advances in Knowledge Discovery and Data Mining. PAKDD 2016, ed. by J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Huang, R. Wang. Lecture Notes in Computer Science, vol. 9652 (Springer, Cham, 2016)

    Chapter  Google Scholar 

  41. S. Poslad, Ubiquitous Computing: Smart Devices, Environments and Interactions (Wiley, New York, 2011)

    Google Scholar 

  42. S. Rifai, Y. Bengio, Y. Dauphin, P. Vincent, A generative process for sampling contractive auto-encoders (2012). Preprint arXiv:1206.6434

    Google Scholar 

  43. F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)

    Article  Google Scholar 

  44. R. Salakhutdinov, A. Mnih, G. Hinton, Restricted Boltzmann machines for collaborative filtering, in International Conference on Machine Learning (2007), pp. 791–798

    Google Scholar 

  45. J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2014)

    Article  Google Scholar 

  46. J. Schmidhuber, Learning complex, extended sequences using the principle of history compression. Neural Comput. 4(2), 234–242 (2014)

    Article  Google Scholar 

  47. M. Shah, C. Rubadue, D. Suster, D. Wang, Deep learning assessment of tumor proliferation in breast cancer histological images (2016). Preprint arXiv:1610.03467

    Google Scholar 

  48. H.-C.C. Shin, M.R. Orton, D.J. Collins, S.J. Doran, M.O. Leach, 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(8), 1930–1943 (2013)

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  50. B. Wei, Z. Han, X. He, Y. Yin, Deep learning model based breast cancer histopathological image classification, in 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (IEEE, Piscataway, 2017), pp. 348–353

    Google Scholar 

  51. M.N. Wernick, Y. Yang, J.G. Brankov, G. Yourganov, S.C. Strother, Machine learning in medical imaging. IEEE Signal Process. Mag. 27(4), 25–38 (2010)

    Article  Google Scholar 

  52. S. Zhang, J. Zhou, H. Hu, H. Gong, L. Chen, C. Cheng, J. Zeng, A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res. 44(4), e32 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Murat Simsek

  2. Department of Astronautical Engineering, Istanbul Technical University, Istanbul, Turkey

    Murat Simsek

  3. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Alex Adim Obinikpo & Burak Kantarci

Authors
  1. Murat Simsek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alex Adim Obinikpo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Burak Kantarci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Burak Kantarci .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Abdulmotaleb El Saddik

  2. Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    M. Shamim Hossain

  3. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Burak Kantarci

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Simsek, M., Obinikpo, A.A., Kantarci, B. (2020). Deep Learning in Smart Health: Methodologies, Applications, Challenges. In: El Saddik, A., Hossain, M., Kantarci, B. (eds) Connected Health in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-27844-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-27844-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27843-4

  • Online ISBN: 978-3-030-27844-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation