Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: A Review

  • Saroj Kumar PandeyEmail author
  • Rekh Ram Janghel


The concept of deep learning originates from artificial neural networks which has become a very popular research area during the past few decades. There are two main reasons for for the wide acceptance of deep learning. First one being the overfitting problem has been partially resolved with the advent of big data analytics techniques. The second point for wide acceptance of deep learning is that deep neural networks undergo pre-training procedure before unsupervised learning, which assigns some initial values to the network. This article describes the all deep learning techniques and their experimental analysis with advantage and disadvantages. This review highlights the till date progress of the six deep learning techniques namely, autoencoder, restricted Boltzmann machine, deep belief network, recurrent neural network, convolutional neural network, and generative adversarial network with practical variant case studies. A wide discourse has been taken into consideration for the survey in the article. It concludes try to reflect some of the most fundamental and recent applications in the medical health-care system, and also identify some of the challenges and opportunities of the deep learning techniques.


Restricted Boltzmann machine Deep belief network Convolutional neural network Autoencoder Generative adversarial network Recurrent neural network 



  1. 1.
    Li D (2014) A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process 3:e2Google Scholar
  2. 2.
    Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127MathSciNetzbMATHGoogle Scholar
  3. 3.
    Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554MathSciNetzbMATHGoogle Scholar
  4. 4.
    Hinton GE (2006) Reducing the dimensionality of data with neural networks. Science (80) 313(5786):504–507MathSciNetzbMATHGoogle Scholar
  5. 5.
    Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19(1):153Google Scholar
  6. 6.
    Silver D et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489Google Scholar
  7. 7.
    Hinton RSZ, Geoffrey E (1994) Autoencoders, minimum description length and Helmholtz free energy. Adv Neural Inf Process Syst 3(3):219Google Scholar
  8. 8.
    Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science (80) 313(5786):504–507MathSciNetzbMATHGoogle Scholar
  9. 9.
    Meyer D (2015) Introduction to autoencodersGoogle Scholar
  10. 10.
    Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828Google Scholar
  11. 11.
    Dietterich T (1995) Overfitting and undercomputing in machine learning. ACM Comput Surv 27(3):326–327Google Scholar
  12. 12.
    Palm RB (2012) Prediction as a candidate for learning deep hierarchical models of data. Technical University of Denmark, 5Google Scholar
  13. 13.
    Pérez-Cruz F (2008) Kullback-Leibler divergence estimation of continuous distributions. In: Information theory, 2008. ISIT 2008. IEEE international symposium on. IEEE, pp 1666–1670Google Scholar
  14. 14.
    Widrow B, Lehr MA (1990) 30 Years of adaptive neural networks: perceptron, madaline, and backpropagation. Proc IEEE 78(9):1415–1442Google Scholar
  15. 15.
    Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning (ICML 2008), pp 1096–1103Google Scholar
  16. 16.
    Rifai S, Muller X (2011) Contractive auto-encoders: explicit invariance during feature extraction. ICML 85(1):833–840Google Scholar
  17. 17.
    Makhzani A, Frey B (2013) K-sparse autoencoders. arXiv preprint arXiv:1312.5663
  18. 18.
    Sun M, Zhang X, Van Hamme H, Zheng TF (2016) Unseen noise estimation using separable deep auto encoder for speech enhancement. IEEE ACM Trans Speech Lang Process 24(1):93–104Google Scholar
  19. 19.
    Ackley DH, Hinton GE, Sejnowski TJ (1985) A learning algorithm for Boltzmann machines. Cogn Sci 9:147–169Google Scholar
  20. 20.
    Hinton G (2010) A practical guide to training restricted Boltzmann machines a practical guide to training restricted Boltzmann machines. Computer (Long. Beach. Calif) 9(3):1Google Scholar
  21. 21.
    Hinton GE (2002) Training products of experts by minimizing contrastive divergence. Neural Comput 14(8):1771–1800zbMATHGoogle Scholar
  22. 22.
    Fischer A, Igel C (2012) An introduction to restricted Boltzmann machines. Lect Notes Comput Sci Prog Pattern Recogn Image Anal Comput Vis Appl 7441:14–36Google Scholar
  23. 23.
    Bengio Y, Delalleau O (2009) Justifying and generalizing contrastive divergence. Neural Comput 21(6):1601–1621MathSciNetzbMATHGoogle Scholar
  24. 24.
    Sutskever I, Tieleman T (2010) On the convergence properties of contrastive divergence. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 789–795Google Scholar
  25. 25.
    Fischer A, Igel C (2011) Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives. In: ESANNGoogle Scholar
  26. 26.
    Mnih V, Larochelle H, Hinton GE (2012) Conditional restricted boltzmann machines for structured output prediction. arXiv preprint arXiv:1202.3748
  27. 27.
    Larochelle H, Bengio Y (2008) Classification using discriminative restricted Boltzmann machines. In: ICML, pp 536–543Google Scholar
  28. 28.
    Hinton GE (2012) A practical guide to training restricted boltzmann machines BT. In: Montavon G, Orr GB, Mller K-R (eds) Neural Networks: tricks of the trade, Second edn. Springer, Berlin, pp 599–619Google Scholar
  29. 29.
    Tang Y, Salakhutdinov R, Hinton G (2012) Robust Boltzmann machines for recognition and denoising. In: Proceedings of the IEEE conference on computer vision and pattern Recognition, pp 2264–2271Google Scholar
  30. 30.
    Keyvanrad MA, Homayounpour MM A (2014, September) brief survey on deep belief networks and introducing a new object oriented MATLAB toolbox (DeeBNet). In: CoRR, vol. abs/1408.3, pp 0–25Google Scholar
  31. 31.
    Deng L (2013) Three classes of deep learning architectures and their applications: a tutorial survey. Microsoft Research, WashingtonGoogle Scholar
  32. 32.
    Arnold L, Rebecchi S, Chevallier S, Paugam-Moisy H (2011) An introduction to deep learning. In: European Symposium on Artificial Neural Networks (ESANN), Apr 2011, Bruges, Belgium. Proceedings of the European Symposium on Artificial Neural NetworksGoogle Scholar
  33. 33.
    Plis SM et al (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229Google Scholar
  34. 34.
    Hinton GE, Dayan P, Frey BJ, Neal RM (1995) The “wake-sleep” algorithm for unsupervised neural networks. Science (80) 268(5214):1158–1161Google Scholar
  35. 35.
    Tang A, Lu K, Wang Y, Huang J, Li H (2015) A real-time hand posture recognition system using deep neural networks. ACM Trans Intell Syst Technol 6(2):21:1–21:23Google Scholar
  36. 36.
    Salakhutdinov R, Hinton G (2008) Using deep belief nets to learn covariance kernels for Gaussian processes. Adv Neural Inf Process Syst (20) 20:1–8Google Scholar
  37. 37.
    Nair V, Hinton GE (2009) 3D object recognition with deep belief nets. Adv Neural Inf Process Syst 21:1339–1347Google Scholar
  38. 38.
    Kuremoto T, Kimura S, Kobayashi K, Obayashi M (2014) Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 137:47–56Google Scholar
  39. 39.
    Liao B, Xu J, Lv J, Zhou S (2015) An image retrieval method for binary images based on DBN and Softmax classifier. IETE Tech Rev 32(4):294–303Google Scholar
  40. 40.
    Abdel-Zaher AM, Eldeib AM (2016) Breast cancer classification using deep belief networks. Expert Syst Appl 46:139–144Google Scholar
  41. 41.
    Qin M, Li Z, Du Z (2017) Red tide time series forecasting by combining ARIMA and deep belief network. Knowl Based Syst 125:39–52Google Scholar
  42. 42.
    Mandic DP, Chambers JA (2001) Recurrent neural networks for prediction: learning algorithms, architectures and stability. Wiley, New York, pp 171–198Google Scholar
  43. 43.
    Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166Google Scholar
  44. 44.
    Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780Google Scholar
  45. 45.
    Sak H, Senior A, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth annual conference of the international speech communication associationGoogle Scholar
  46. 46.
    Chung J, Gulcehre C, Cho K, Bengio Y (2015, June) Gated feedback recurrent neural networks. In: International conference on machine learning, pp 2067–2075Google Scholar
  47. 47.
    Yildirim O (2018) A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput Biol Med 96:189–202Google Scholar
  48. 48.
    Jin B, Che C, Liu Z, Zhang S, Yin X, Wei X (2018) Predicting the risk of heart failure with EHR sequential data modeling. IEEE Access 6:9256–9261Google Scholar
  49. 49.
    Zhao A, Qi L, Li J, Dong J, Yu H (2018, April) LSTM for diagnosis of neurodegenerative diseases using gait data. In: Ninth international conference on graphic and image processing (ICGIP 2017), international society for optics and photonics, vol 10615, p 106155BGoogle Scholar
  50. 50.
    Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195:215–243Google Scholar
  51. 51.
    Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202zbMATHGoogle Scholar
  52. 52.
    Lang KJ, Waibel AH, Hinton GE (1990) A time-delay neural network architecture for isolated word recognition. Neural Netw 3(1):23–43Google Scholar
  53. 53.
    Le Cun Jackel LD, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Le Cun B, Denker J, Henderson D (1990) Handwritten digit recognition with a back-propagation network. Adv Neural Inf Process Syst 2:396–404Google Scholar
  54. 54.
    Hecht-Nielsen R (1989) Theory of the backpropagation neural network. Proc Int Jt Conf Neural Netw 1:593–605Google Scholar
  55. 55.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2323Google Scholar
  56. 56.
    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv. Neural Inf Process Syst 25:1–9Google Scholar
  57. 57.
    Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning, no. 3, pp 807–814Google Scholar
  58. 58.
    Boureau Y-L, Ponce J, LeCun Y (2010) A theoretical analysis of feature pooling in visual recognition. In: ICML, pp 111–118Google Scholar
  59. 59.
    Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M (2014) Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806
  60. 60.
    Eigen D, Rolfe J, Fergus R, LeCun Y (2013) Understanding deep architectures using a recursive convolutional network. arXiv preprint arXiv:1312.1847
  61. 61.
    Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, Cham, pp 818–833Google Scholar
  62. 62.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9Google Scholar
  63. 63.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778Google Scholar
  64. 64.
    Ponti MA, Ribeiro LS, Nazare TS, Bui T, Collomosse J (2018) Everything you wanted to know about deep learning for computer vision but were afraid to ask. In: 2017 30th SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T)Google Scholar
  65. 65.
    Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680Google Scholar
  66. 66.
    Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434
  67. 67.
    Huang X, Li Y, Poursaeed O, Hopcroft J, Belongie S (2017) Stacked generative adversarial networks. IEEE Conf Comput Vis Pattern Recogn 2:4Google Scholar
  68. 68.
    Goodfellow I (2016) NIPS 2016 tutorial: generative adversarial networks. arXiv preprint arXiv:1701.00160
  69. 69.
    Zhai S, Cheng Y, Feris R, Zhang Z (2016) Generative adversarial networks as variational training of energy based models. arXiv preprint arXiv:1611.01799
  70. 70.
    Reed S, Akata Z, Yan X, Logeswaran L, Schiele B, Lee H (2016) Generative adversarial text to image synthesis. arXiv preprint arXiv:1605.05396
  71. 71.
    Kamnitsas K, Baumgartner C, Ledig C, Newcombe V, Simpson J, Kane A, Glocker B (2017, June) Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: International conference on information processing in medical imaging. Springer, Cham, pp 597–609Google Scholar
  72. 72.
    Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. arXiv preprintGoogle Scholar
  73. 73.
    Al Rahhal MM et al (2016) Deep learning approach for active classification of electrocardiogram signals. Inf Sci 345:340–354Google Scholar
  74. 74.
    Kooi T et al (2017) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303–312Google Scholar
  75. 75.
    Ching T et al (2017, May) Opportunities and obstacles for deep learning in biology and medicine. bioRxivGoogle Scholar
  76. 76.
    Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. J Big Data 2(1):1Google Scholar
  77. 77.
    Suthaharan S (2014) Big data classification. ACM Sigmetrics Perform Eval Rev 41(4):70–73Google Scholar
  78. 78.
    Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th international conference on machine learning, no. 1, pp 513–520Google Scholar
  79. 79.
    Chen X-W, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525Google Scholar
  80. 80.
    Brosch T, Tam R, Alzheimers Disease Neuroimaging Initiative (2013, September) Manifold learning of brain MRIs by deep learning. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 633–640Google Scholar
  81. 81.
    Suk HI, Shen D (2013, September) Deep learning-based feature representation for AD/MCI classification. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 583–590Google Scholar
  82. 82.
    Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Calhoun VD (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229Google Scholar
  83. 83.
    Yang D, Zhang S, Yan Z, Tan C, Li K, Metaxas D (2015, April) Automated anatomical landmark detection ondistal femur surface using convolutional neural network. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI), pp 17–21Google Scholar
  84. 84.
    Zheng Y, Liu D, Georgescu B, Nguyen H, Comaniciu D (2015, October) 3D deep learning for efficient and robust landmark detection in volumetric data. In International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 565–572Google Scholar
  85. 85.
    Ghesu FC, Georgescu B, Mansi T, Neumann D, Hornegger J, Comaniciu D (2016, October) An artificial agent for anatomical landmark detection in medical images. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 229–237Google Scholar
  86. 86.
    Baumgartner CF, Kamnitsas K, Matthew J, Smith S, Kainz B, Rueckert D (2016, October) Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 203–211Google Scholar
  87. 87.
    Chen H, Dou Q, Ni D, Cheng JZ, Qin J, Li S, Heng PA (2015, October) Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 507–514Google Scholar
  88. 88.
    Chen J, Yang L, Zhang Y, Alber M, Chen DZ (2016) Combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation. In: Advances in neural information processing systems, pp 3036–3044Google Scholar
  89. 89.
    Xie Y, Zhang Z, Sapkota M, Yang L (2016, October) Spatial clockwork recurrent neural network for muscle perimysium segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 185–193Google Scholar
  90. 90.
    Wu G, Kim M, Wang Q, Gao Y, Liao S, Shen D (2013, September) Unsupervised deep feature learning for deformable registration of MR brain images. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 649–656Google Scholar
  91. 91.
    Cheng X, Zhang L, Zheng Y (2018) Deep similarity learning for multimodal medical images. Comput Methods Biomech Biomed Eng Imaging Vis 6(3):248–252Google Scholar
  92. 92.
    Krebs J, Mansi T, Delingette H, Zhang L, Ghesu FC, Miao S, Kamen A (2017, September) Robust non-rigid registration through agent-based action learning. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 344–352Google Scholar
  93. 93.
    Simonovsky M, Gutirrez-Becker B, Mateus D, Navab N, Komodakis N (2016) A deep metric for multimodal registration. In: Proceedings of the medical image computing and computer-assisted intervention. Lecture notes in computer science, 9902, pp 10–18Google Scholar
  94. 94.
    Greenspan H, van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35(5):1153–1159Google Scholar
  95. 95.
    Nie D, Zhang H, Adeli E, Liu L, Shen D (2016) 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients BT-medical image computing and computer-assisted intervention. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Proceeding of the MICCAI 2016: 19th international conference, Athens, Greece, 17–21 October 2016. Springer International Publishing, Cham, pp 212–220Google Scholar
  96. 96.
    Sun W, Tseng TB, Zhang J, Qian W (2017) Computerized medical imaging and graphics enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Graph 57:4–9Google Scholar
  97. 97.
    Majumdar A, Singhal V (2017) Noisy deep dictionary learning: application to Alzheimer’s Disease classification. In: Neural networks (IJCNN), 2017 international joint conference on. IEEE, pp 2679–2683Google Scholar
  98. 98.
    Avendi MR, Kheradvar A, Jafarkhani H (2016) A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal 30:108–119Google Scholar
  99. 99.
    Havaei M, Guizard N, Larochelle H, Jodoin PM (2016) Deep learning trends for focal brain pathology segmentation in MRI. In: Machine learning for health informatics. Springer, Cham, pp 125–148Google Scholar
  100. 100.
    Andreu-Perez J, Poon CCY, Merrifield RD, Wong STC, Yang GZ (2015) Big data for health. IEEE J Biomed Heal Inform 19(4):1193–1208Google Scholar
  101. 101.
    Rahhal MMA, Bazi Y, Alhichri H, Alajlan N, Melgani F, Yager RR (2016) Deep learning approach for active classification of electrocardiogram signals. Inf Sci (Ny) 345:340–354Google Scholar
  102. 102.
    Liang Z, Zhang G, Huang JX, Hu QV (2014) Deep learning for healthcare decision making with EMRs. In: Bioinformatics and Biomedicine (BIBM), 2014 IEEE International conference on. IEEE, pp 556–559Google Scholar
  103. 103.
    Futoma J, Morris J, Lucas J (2015) A comparison of models for predicting early hospital readmissions. J Biomed Inform 56:229–238Google Scholar
  104. 104.
    Yan Y, Qin X, Wu Y, Zhang N, Fan J, Wang L (2015) A restricted Boltzmann machine based two-lead electrocardiography classification. In: 2015 IEEE 12th international conference on wearable and implantable body sensor networks (BSN)Google Scholar
  105. 105.
    Che Z, Purushotham S, Khemani R, Liu Y (2015) Distilling knowledge from deep networks with applications to healthcare domain. arXiv preprint arXiv:1512.03542
  106. 106.
    Huang T, Lan L, Fang X, An P, Min J, Wang F (2015) Promises and challenges of big data computing in health sciences. Big Data Res 2(1):2–11Google Scholar
  107. 107.
    Ong BT, Sugiura K, Zettsu K (2016) Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5. Neural Comput. Appl 27(6):1553–1566Google Scholar
  108. 108.
    Zhao L, Chen J, Chen F, Wang W, Lu CT, Ramakrishnan N (2016) SimNest: social media nested epidemic simulation via online semi-supervised deep learning. Proc IEEE Int Conf Data Min i:639–648Google Scholar
  109. 109.
    Han S, Mao H, Dally WJ (2015) Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv preprint arXiv:1510.00149
  110. 110.
    Kim Y-D et al (2015) Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530
  111. 111.
    Lin Y et al (2017) Deep gradient compression: Reducing the communication bandwidth for distributed training. arXiv preprint arXiv:1712.01887

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Authors and Affiliations

  1. 1.Department of Information TechnologyNIT, RaipurRaipurIndia

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