Deep Learning Applications and Challenges for Healthcare System: A Review
Keywords:
Deep Learning, Artificial Intelligent, Healthcare, Covid_19, Automation
Abstract
Deep Learning is recent biz word in artificial intelligent as well as it is the third wave of artificial intelligence research. World is rapidly growing toward the automation almost every sector is going automated their services, products and industry through AI. The recent research is indicating that deep learning application and challenges for healthcare system is big challenge although so many improvement and advancement in healthcare system in the past few decades. In the recent covid_19 pandemic has indicated so many loop holes in the healthcare automation system worldwide. Our discussion focused on deep learning automation healthcare system. This paper aims to provide to identify the more challenges and application of deep learning in healthcare system, as automation and ease of system is forever directly comparative to success of any system. Our work will enable researchers and professionals to know deep learning application challenges in healthcare system critical analysis on tools proposed.
References
[1] Igbe Tobore et al., “Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations”, JMIR, 2019.NCBI,USA
[2] Eui Jin Hwang et al., “Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges” 2020 NCBI, USA
[3] Riccardo Miotto et al.,“Deep learning for healthcare: review, opportunities and challenges ”, 2017 in Oxford
[4] Ripal Nathuji et al, “Deep learning applications and challenges in big data analytics”, ACM 2015, Springeropen
[5] Connor Shorten et al., “Deep Learning applications for COVID-19”Springer, 2019.
[6] Justin Ker, et al, “Deep Learning Applications in Medical Image Analysis
”, IEEE 2017 Canada
[7] Hamidreza Bolhasni et al. “Deep Learning Application for IoT in healthcare system: A Review ”, Elsevier, 2021
[8] Hamidreza Bolhasni et al, “Deep Learning Application for IoT in Healthcare: A Review, , ELSEVIER 2021
[9] Tianming Zhao at al., “A Survey of Deep Learning on Mobile Devices: Applications, IEEE, 2022
[10] Khan Muhammad at al., “Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey”, IEEE 2020
[11] https://www.mathworks.com/discovery/deep-learning.htm
[12] https://machinelearningmastery.com/what-is-deep-learning/
[13] Chintala. convnet-benchmarks. https://github.com/soumith/convnet-benchmarks
[14] F. Chollet. Keras. http://keras.io/.
[15] D. P. Kingma and M. Welling. Auto-encoding variational bayes. ICLR, 2014.
[16] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional
[17] neural networks. In NIPS, pages 1106–1114, 2012.
[18] Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521:436–444, 2012
[19] T. Sercu, C. Puhrsch, B. Kingsbury, and Y. LeCun. Very deep multilingual convolutional neural networks for LVCSR. CoRR, abs/1509.08967, 2015.
[20] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. CoRR, abs/1312.6229, 2013.
[21] I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. NIPS, pages 3104–3112, 2014.
[22] T. Tieleman and G. Hinton. Lecture 6.5-RMSprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4, 2012.
[23] S. Tokui. Chainer. http://chainer.org/.
[24] A. Toshev and C. Szegedy. Deeppose: Human pose estimation via deep neural networks. In CVPR, pages 1653–1660, 2014.
[25] O. Vinyals and Q. V. Le. A neural conversational model. CoRR, abs/1506.05869, 2015.
[26] P. J. Werbos. Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1(4):339–356, 1988
[27] F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, and Y. Bengio. Theano: new features and speed improvements. NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2012.
[28] L. Bottou. Stochastic gradient tricks. In Neural Networks, Tricks of the Trade, Reloaded, pages 430–445. Springer, 2012.
[29] S. Chintala. convnet-benchmarks. https://github.com/soumith/convnet-benchmarks.
[30] F. Chollet. Keras. http://keras.io/. 5
[31] R. Collobert. Torch. NIPS Workshop on Machine Learning Open Source Software, 2008.
[32] J. C. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121–2159, 2011.
[33] I. J. Goodfellow, D. Warde-Farley, P. Lamblin, V. Dumoulin, M. Mirza, R. Pascanu, J. Bergstra, F. Bastien, and Y. Bengio. Pylearn2: a machine learning research library. CoRR, abs/1308.4214, 2013.
[34] Y. Jia. Caffe: An open source convolutional architecture for fast feature embedding, 2013.
[35] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014
[2] Eui Jin Hwang et al., “Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges” 2020 NCBI, USA
[3] Riccardo Miotto et al.,“Deep learning for healthcare: review, opportunities and challenges ”, 2017 in Oxford
[4] Ripal Nathuji et al, “Deep learning applications and challenges in big data analytics”, ACM 2015, Springeropen
[5] Connor Shorten et al., “Deep Learning applications for COVID-19”Springer, 2019.
[6] Justin Ker, et al, “Deep Learning Applications in Medical Image Analysis
”, IEEE 2017 Canada
[7] Hamidreza Bolhasni et al. “Deep Learning Application for IoT in healthcare system: A Review ”, Elsevier, 2021
[8] Hamidreza Bolhasni et al, “Deep Learning Application for IoT in Healthcare: A Review, , ELSEVIER 2021
[9] Tianming Zhao at al., “A Survey of Deep Learning on Mobile Devices: Applications, IEEE, 2022
[10] Khan Muhammad at al., “Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey”, IEEE 2020
[11] https://www.mathworks.com/discovery/deep-learning.htm
[12] https://machinelearningmastery.com/what-is-deep-learning/
[13] Chintala. convnet-benchmarks. https://github.com/soumith/convnet-benchmarks
[14] F. Chollet. Keras. http://keras.io/.
[15] D. P. Kingma and M. Welling. Auto-encoding variational bayes. ICLR, 2014.
[16] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional
[17] neural networks. In NIPS, pages 1106–1114, 2012.
[18] Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521:436–444, 2012
[19] T. Sercu, C. Puhrsch, B. Kingsbury, and Y. LeCun. Very deep multilingual convolutional neural networks for LVCSR. CoRR, abs/1509.08967, 2015.
[20] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. CoRR, abs/1312.6229, 2013.
[21] I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. NIPS, pages 3104–3112, 2014.
[22] T. Tieleman and G. Hinton. Lecture 6.5-RMSprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4, 2012.
[23] S. Tokui. Chainer. http://chainer.org/.
[24] A. Toshev and C. Szegedy. Deeppose: Human pose estimation via deep neural networks. In CVPR, pages 1653–1660, 2014.
[25] O. Vinyals and Q. V. Le. A neural conversational model. CoRR, abs/1506.05869, 2015.
[26] P. J. Werbos. Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1(4):339–356, 1988
[27] F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, and Y. Bengio. Theano: new features and speed improvements. NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2012.
[28] L. Bottou. Stochastic gradient tricks. In Neural Networks, Tricks of the Trade, Reloaded, pages 430–445. Springer, 2012.
[29] S. Chintala. convnet-benchmarks. https://github.com/soumith/convnet-benchmarks.
[30] F. Chollet. Keras. http://keras.io/. 5
[31] R. Collobert. Torch. NIPS Workshop on Machine Learning Open Source Software, 2008.
[32] J. C. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12:2121–2159, 2011.
[33] I. J. Goodfellow, D. Warde-Farley, P. Lamblin, V. Dumoulin, M. Mirza, R. Pascanu, J. Bergstra, F. Bastien, and Y. Bengio. Pylearn2: a machine learning research library. CoRR, abs/1308.4214, 2013.
[34] Y. Jia. Caffe: An open source convolutional architecture for fast feature embedding, 2013.
[35] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014
Published
2023-01-31
How to Cite
Ahmed, S., Bhutto, A., & Bashir, F. (2023). Deep Learning Applications and Challenges for Healthcare System: A Review. International Journal of Artificial Intelligence & Mathematical Sciences, 1(2), 1-6. https://doi.org/10.58921/ijaims.v1i2.34
