Overview of Deep Learning Models in Biomedical Domain with the Help of R Statistical Software

Open access

Abstract

With the increase in volume of data and presence of structured and unstructured data in the biomedical filed, there is a need for building models which can handle complex & non-linear relations in the data and also predict and classify outcomes with higher accuracy. Deep learning models are one of such models which can handle complex and nonlinear data and are being increasingly used in the biomedical filed in the recent years. Deep learning methodology evolved from artificial neural networks which process the input data through multiple hidden layers with higher level of abstraction. Deep Learning networks are used in various fields such as image processing, speech recognition, fraud deduction, classification and prediction. Objectives of this paper is to provide an overview of Deep Learning Models and its application in the biomedical domain using R Statistical software Deep Learning concepts are illustrated by using the R statistical software package. X-ray Images from NIH datasets used to explain the prediction accuracy of the deep learning models. Deep Learning models helped to classify the outcomes under study with 91% accuracy. The paper provided an overview of Deep Learning Models, its types, its application in biomedical domain. This paper has shown the effect of deep learning network in classifying images into normal and disease with 91% accuracy with help of the R statistical package.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Goodfellow I. Bengio Y. Courville A. & Bengio Y. (2016). Deep learning (Vol. 1). Cambridge: MA USA MIT press.

  • 2. LeCun Y. Bengio Y. & Hinton G. (2015). Deep learning. Nature 521(7553) 436.

  • 3. Miotto R. Wang F. Wang S. Jiang X. & Dudley J. T. (2017). Deep learning for healthcare: review opportunities and challenges. Briefings in bioinformatics. 1-11.

  • 4. Urban G. Bache K. M. Phan D. Sobrino A. Shmakov A. K. Hachey S. J. ... & Baldi P. (2018). Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images. IEEE/ ACM Transactions on Computational Biology and Bioinformatics.

  • 5. Lakhani P. (2017). Deep convolutional neural networks for endotracheal tube position and X-ray image classification: challenges and opportunities. Journal of digital imaging 30(4) 460-468.

  • 6. LeCun Y. & Bengio Y. (1995). Convolutional networks for images speech and time series. The handbook of brain theory and neural networks 3361(10). 1995

  • 7. Gao M. Bagci U. Lu L. Wu A. Buty M. Shin H. C. ...& Xu Z. (2018). Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(1) 1-6.

  • 8. Mohamed A. A. Berg W. A. Peng H. Luo Y. Jankowitz R. C. & Wu S. (2018). A deep learning method for classifying mammographic breast density categories. Medical physics 45(1) 314-321

  • 9. Esteva A. Kuprel B. Novoa R. A. Ko J. Swetter S. M. Blau H. M. & Thrun S. (2017). Dermatologistlevel classification of skin cancer with deep neural networks. Nature 542(7639) 115

  • 10. Saltz Joel Rajarsi Gupta Le Hou Tahsin Kurc Pankaj Singh Vu Nguyen Dimitris Samaras et al. “Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images.” Cell reports 23 no. 1 (2018): 181.

  • 11. Kermany Daniel S. Michael Goldbaum Wenjia Cai Carolina CS Valentim Huiying Liang Sally L. Baxter Alex McKeown et al. “Identifying medical diagnoses and treatable diseases by image-based deep learning.” Cell 172 no. 5 (2018): 1122-1131.

  • 12. Gerard S. E. Patton T. J. Christensen G. E. Bayouth J. E. & Reinhardt J. M. (2018). FissureNet: A deep learning approach for pulmonary fissure detection in CT images. IEEE transactions on medical imaging. 1-1.

  • 13. Hinton G. (2018). Deep learning-a technology with the potential to transform health care. JAMA. 320(11):1101-1102.

  • 14. Chen M. Hao Y. Hwang K. Wang L. & Wang L. (2017). Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5 8869-8879.

  • 15. Miotto R. Li L. Kidd B. A. & Dudley J. T. (2016). Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific reports 6 26094.

  • 16. Liu S. Liu S. Cai W. Pujol S. Kikinis R. & Feng D. (2014 April). Early diagnosis of Alzheimer’s disease with deep learning. In Biomedical Imaging (ISBI) 2014 IEEE 11th International Symposium on (pp. 1015-1018). IEEE.

  • 17. Alipanahi B. Delong A. Weirauch M. T. & Frey B. J. (2015). Predicting the sequence specificities of DNAand RNA-binding proteins by deep learning. Nature biotechnology 33(8) 831.

  • 18. Park Y. & Kellis M. (2015). Deep learning for regulatory genomics. Nature biotechnology 33(8) 825.

  • 19. Chen Y. Li Y. Narayan R. Subramanian A. & Xie X. (2016). Gene expression inference with deep learning. Bioinformatics 32(12) 1832-1839.

  • 20. Weng W. H. Wagholikar K. B. McCray A. T. Szolovits P. & Chueh H. C. (2017). Medical subdomain classification of clinical notes using a machine learning- based natural language processing approach. BMC medical informatics and decision making 17(1) 155.

  • 21. Collins F. S. & Varmus H. (2015). A new initiative on precision medicine. New England Journal of Medicine 372(9) 793-795.

  • 22. Nezhad M. Z. Zhu D. Li X. Yang K. & Levy P. (2016). Safs: A deep feature selection approach for precision medicine. In Bioinformatics and Biomedicine (BIBM) IEEE International Conference 15-18 Dec 2016 (pp. 501-506). Shenzhen China IEEE.

  • 23. Lu L. Zheng Y. Carneiro G. & Yang L. (2017). Deep Learning and Convolutional Neural Networks for Medical Image Computing. (1st Ed) MA USA. Springer.

  • 24. Lo S. C. B. Chan H. P. Lin J. S. Li H. Freedman M. T. & Mun S. K. (1995). Artificial convolution neural network for medical image pattern recognition. Neural networks 8(7-8) 1201-1214.

  • 25. Ciresan D. C. Meier U. Gambardella L. M. & Schmidhuber J. (2011 September). Convolutional neural network committees for handwritten character classification. In Document Analysis and Recognition (ICDAR) 2011 International Conference 18-21 Sept. 2011 (pp. 1135-1139). Beijing China IEEE.

  • 26. Mou L. Ghamisi P. & Zhu X. X. (2017). Deep recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 55(7) 3639-3655.

  • 27. Tran S. D. & Manmatha R. (2018). U.S. Patent No. 9892344. Washington DC: U.S. Patent and Trademark Office.

  • 28. Zhang Y. & Shi B. (2017). Improving pooling method for regularization of convolutional networks based on the failure probability density. Optik-International Journal for Light and Electron Optics 145 258-265.

  • 29. Del Fiol G. Michelson M. Iorio A. Cotoi C. & Haynes R. B. (2018). A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study. Journal of medical Internet research 20(6).

  • 30. Choi K. Fazekas G. Sandler M. & Cho K. (2017 March). Convolutional recurrent neural networks for music classification. In Acoustics Speech and Signal Processing (ICASSP) 2017 IEEE International Conference 5-9 March 2017 (pp. 2392-2396). New Orleans USA IEEE.

  • 31. Chen L. C. Papandreou G. Kokkinos I. Murphy K. & Yuille A. L. (2018). Deeplab: Semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40(4) 834-848.

  • 32. Kimmel J. Brack A. & Marshall W. F. (2017). Deep convolution neural networks allow analysis of cell motility during stem cell differentiation and neoplastic transformation. bioRxiv 159202.

  • 33. Graves A. Mohamed A. R. & Hinton G. (2013 May). Speech recognition with deep recurrent neural networks. In Acoustics speech and signal processing (icassp) IEEE international conference 26-31 May 2013 (pp. 6645-6649). Vancouver Canada IEEE.

  • 34. Sun X. Li T. Li Y. Li Q. Huang Y. & Liu J. (2018). Recurrent neural system with minimum complexity: A deep learning perspective. Neurocomputing 275 1333-1349.

  • 35. Tan J. H. Hagiwara Y. Pang W. Lim I. Oh S. L. Adam M. ... & Acharya U. R. (2018). Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Computers in Biology and Medicine. 9419-26.

  • 36. Lipton Z. C. Kale D. C. Elkan C. & Wetzel R. (2015). Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677.

  • 37. Jnawali K. Arbabshirani M. R. Rao N. & Patel A. A. (2018 February). Deep 3D convolution neural network for CT brain hemorrhage classification. In Medical Imaging 2018: Computer-Aided Diagnosis (Vol. 10575 p. 105751C).

  • 38. Acharya U. R. Oh S. L. Hagiwara Y. Tan J. H. & Adeli H. (2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine 100 270-278

  • 39. Li W. Shi S. Gao Z. Wei W. Zhu Q. Lin X. ... & Gao S. (2018 March). Improved deep belief network model and its application in named entity recognition of Chinese electronic medical records. In Big Data Analysis (ICBDA) 2018 IEEE 3rd International Conference9-12 March 2018(pp. 356-360). Shanghai China IEEE.

  • 40. Shickel B. Tighe P. J. Bihorac A. & Rashidi P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics 22(5) 1589-1604

  • 41. Zech J. Pain M. Titano J. Badgeley M. Schefflein J. Su A. ... & Oermann E. K. (2018). Natural Language- based Machine Learning Models for the Annotation of Clinical Radiology Reports. Radiology 287(2) 570-580.

  • 42. Del Fiol G. Michelson M. Iorio A. Cotoi C. & Haynes R. B. (2018). A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study. Journal of medical Internet research 20(6).

  • 43. Young T. Hazarika D. Poria S. & Cambria E. (2018). Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine 13(3) 55-75.

  • 44. Deng L. & Liu Y. (Eds.). (2018). Deep Learning in Natural Language Processing. MA USA Springer.

  • 45. R Core Team (August 2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria. Retrieved September 21 2018 from http://www.R-project.org/.

  • 46. Chen T Kou Q He T. mxnet. MXNet [2015]. Retrieved September 21 2018 from https://github.com/dmlc/mxnet/R-package

  • 47. Wang X Peng Y Lu L Lu Z Bagheri M Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR 21-26 July 21-26 2017( 2097-2106). Honolulu HawaiiIEEE

  • 48. The Digital Database for Screening Mammography Michael Heath Kevin Bowyer Daniel Kopans Richard Moore and W. Philip Kegelmeyer in Proceedings of the Fifth International Workshop on Digital Mammography M.J. Yaffe ed. 212-218 Medical Physics Publishing 2001. ISBN 1-930524-00-5.

  • 49. J Suckling et al (1994): The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 pp375-378.

  • 50. Diagnostic image Analysis Group [2009] Retrieved September 21 2018 from http://www.diagnijmegen.nl/index.php/NWO__Bayesian_Decision_Support_in_Medical_Screening_%28B-SCREEN%29

  • 51. MITOS-ATYPIA [2014] Retrieved September 21 2018 from https://mitos-atypia-14.grand-challenge.org/

  • 52. Pau G Fuchs F Sklyar O Boutros M Huber W (2010). “EBImage-an R package for image processing with applications to cellular phenotypes.” Bioinformatics 26(7) 979-981. doi:

    • Crossref
    • Export Citation
Search
Journal information
Impact Factor


CiteScore 2018: 0.13

SCImago Journal Rank (SJR) 2018: 0.118
Source Normalized Impact per Paper (SNIP) 2018: 0.079

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 131 131 13
PDF Downloads 86 86 4