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

Abstract

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.

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Correspondence to Saroj Kumar Pandey.

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Pandey, S.K., Janghel, R.R. Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: A Review. Neural Process Lett 50, 1907–1935 (2019). https://doi.org/10.1007/s11063-018-09976-2

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Keywords

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