Page 64 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

52 Chapter 2 Deep convolutional neural network in medical image processing

scope for the researchers and a few limitations of the CNN meth-
odology. Authors hope that the chapter will help the researchers
to get a general idea of the present scenario on the application of
deep CNN in the field of medical imaging as well as give scope for
future and further studies.

References
[1] The Future of Healthcare Is Tied to AI Big Data. https://cmte.ieee.org/
futuredirections/2018/05/18/the-future-of-health-care-is-tied-to-big-data/.
(Last Accessed 10 March 2020).
[2] H. Greenspan, B. Van Ginneken, R.M. Summers, Guest editorial deep
learning in medical imaging: overview and future promise of an exciting
new technique, IEEE Trans. Med. Imag. 35 (5) (2016) 1153e1159.
[3] G. Wang, A perspective on deep imaging, Ieee Access 4 (2016) 8914e8924.
[4] Y. Liu, H.D. Cheng, J.H. Huang, Y.T. Zhang, X.L. Tang, J.W. Tian, Y. Wang,
Computer aided diagnosis system for breast cancer based on color
Doppler flow imaging, J. Med. Syst. 36 (6) (2012) 3975e3982.
[5] J. Wan, D. Wang, S.C.H. Hoi, P. Wu, J. Zhu, Y. Zhang, J. Li, Deep learning
for content-based image retrieval: a comprehensive study, in: Proceedings
of the 22nd ACM International Conference on Multimedia, 2014,
November, pp. 157e166.
[6] L. Deng, D. Yu, Deep learning: methods and applications, Found. Trends
Signal Process. 7 (3e4) (2014) 197e387.
[7] M. Alagappan, J.R.G. Brown, Y. Mori, T.M. Berzin, Artificial intelligence in
gastrointestinal endoscopy: the future is almost here, World J.
Gastrointest. Endosc. 10 (10) (2018) 239.
[8] A. Janowczyk, A. Madabhushi, Deep learning for digital pathology image
analysis: a comprehensive tutorial with selected use cases, J. Pathol. Inf. 7
(2016).
[9] P. Lakhani, D.L. Gray, C.R. Pett, P. Nagy, G. Shih, Hello world deep
learning in medical imaging, J. Digit. Imag. 31 (3) (2018) 283e289.
[10] W.L. Curvers, R. Kiesslich, J.J.G.H.M. Bergman, Novel imaging modalities
in the detection of oesophageal neoplasia, Best Pract. Res. Clin.
Gastroenterol. 22 (4) (2008) 687e720.
[11] H. Kasban, M.A.M. El-Bendary, D.H. Salama, A comparative study of
medical imaging techniques, Int. J. Inf. Sci. Intell. Syst. 4 (2) (2015) 37e58.
[12] J.B. Frøkjær, A.M. Drewes, H. Gregersen, Imaging of the gastrointestinal
tract-novel technologies, World J. Gastroenterol. 15 (2) (2009) 160.
[13] L. Zhang, Q. Ji, A Bayesian network model for automatic and interactive
image segmentation, IEEE Trans. Image Process. 20 (9) (2011) 2582e2593.
[14] M.M. Sharma, Brain tumor segmentation techniques: a survey, Brain 4 (4)
(2016) 220e223.
[15] G. Vishnuvarthanan, M.P. Rajasekaran, P. Subbaraj, A. Vishnuvarthanan,
An unsupervised learning method with a clustering approach for tumor
identification and tissue segmentation in magnetic resonance brain
images, Appl. Soft Comput. 38 (2016) 190e212.
[16] T. von Landesberger, D. Basgier, M. Becker, Comparative local quality
assessment of 3D medical image segmentations with focus on statistical
shape model-based algorithms, IEEE Trans. Vis. Comp. Graph. 22 (12)
(2015) 2537e2549.