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Erschienen in:
An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

An Investigation of Machine Learning and Neural Computation Paradigms in the Design of Clinical Decision Support Systems (CDSSs)

verfasst von : Summrina K. Wajid, Amir Hussain, Bin Luo, Kaizhu Huang

Erschienen in: Advances in Brain Inspired Cognitive Systems

Verlag: Springer International Publishing

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Abstract

This paper reviews the state of the art techniques for designing next generation CDSSs. CDSS can aid physicians and radiologists to better analyse and treat patients by combining their respective clinical expertise with complementary capabilities of the computers. CDSSs comprise many techniques from inter-desciplinary fields of medical image acquisition, image processing and pattern recognition, neural perception and pattern classifiers for medical data organization, and finally, analysis and optimization to enhance overall system performance. This paper discusses some of the current challenges in designing an efficient CDSS as well as some of the latest techniques that have been proposed to meet these challenges, primarily, by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest, thus aiding in enhanced medical diagnosis.

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Vorheriges Kapitel Decoding Visual Stimuli in Human Brain by Using Anatomical Pattern Analysis on fMRI Images
Nächstes Kapitel A Retina Inspired Model for High Dynamic Range Image Rendering
Literatur
1.
Musen, M.A., et al.: Biomedical informatics. In: Clinical Decision-Support Systems, 4 Edn. pp. 643–674 (2013)
2.
Meyer-Bäse, A.: Introduction. In: Pattern Recognition in Medical Imaging, pp. 1–13. Academic Press, San Diego (2004)
3.
Romero, E., González, F.: From biomedical image analysis to biomedical image understanding using machine learning. In: Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques, pp. 1–26. IGI Global (2010)
4.
Sundaram, M., et al.: Histogram based contrast enhancement for mammogram images. In: 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), pp. 842–846 (2011)
5.
Siddharth, Gupta, R., Bhateja, V.: A new unsharp masking algorithm for mammography using non-linear enhancement function. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds.) Proceedings of the InConINDIA 2012. AISC, vol. 132, pp. 779–786. Springer, Heidelberg (2012)
6.
Cheng, H., et al.: A novel approach to microcalcification detection using fuzzy logic technique. IEEE Trans. Med. Imag. 17(3), 442–450 (1998)CrossRef
7.
Sutton, M.A., Bezdek, J.: Enhancement and analysis of digital mammograms using fuzzy models. Proc. SPIE. 3240, 179–190 (1997)CrossRef
8.
Leiner, B.J., et al.: Microcalcifications detection system through discrete wavelet analysis and contrast enhancement techniques. In: Electronics, Robotics and Automotive Mechanics Conference, CERMA 2008, vol. 272, p. 276 (2008)
9.
Singh, S., et al.: Performance analysis of mammographic image enhancement techniques for early detection of breast cancer. Adv. Parallel Distrib. Comput. Commun. Comput. Inf. Sci. 203, 439–448 (2011)
10.
Weeratunga, S., Kamath, C.: An investigation of implicit active contours for scientific image segmentation. In: Video Communications and Image Processing, SPIE Electronic Imaging, San Jose (2004)
11.
Khan, A.M., Ravi, S.: Image segmentation methods: a comparative study. Int. J. Soft Comput. Eng. (IJSCE) 3, 2231–2307 (2013)
12.
Taneja, A., et al.: A performance study of image segmentation techniques. In: 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), Noida, pp. 1–6 (2015)
13.
Berry, E.: A Practical Approach to Medical Image Processing. CRC Press, Boca Raton (2007)
14.
Kumar, G., Bhatia, P.K.: A detailed review of feature extraction in image processing systems. In: 2014 Fourth International Conference on Advanced Computing & Communication Technologies (ACCT), Rohtak, pp. 5–12 (2014)
15.
Karahaliou, A.N., et al.: Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications. IEEE Trans. Inf Technol. Biomed. 12(6), 731–738 (2008)CrossRef
16.
Mingqiang, Y., et al.: A survey of shape feature extraction techniques. In: Yin, P.-Y. (ed.) Pattern Recognition Techniques, vol. 1, pp. 3–90. InTechOpen, Rijeka (2008)
17.
Jain, R., et al.: Texture. Machine Vision. McGraw-Hill, Inc, New York (1995)
18.
Li, Q.: Computer-Aided Detection and Diagnosis in Medical Imaging. CRC Press, Boca Raton (2015)CrossRef
19.
Castaneda, C., et al.: Clinical decision support systems for improving diagnostic accuracy and achieving precision Medicine. J. Clin. Bioinf. 5, 1 (2015). 4. PMC. Accessed 3 Jul 2016MathSciNetCrossRef
20.
Bhavsar, H., Ganatra, A.: A comparative study of training algorithms for supervised machine learning. Int. J. Soft Comput. Eng. (IJSCE) 2, 2231–2307 (2012)
21.
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Pearson Education, Upper Saddle River (2002). ISBN 0-201-18075-8
22.
Wu, Z., et al.: Digital mammography image enhancement using improved unsharp masking approach. In: 2010 3rd International Congress on Image and Signal Processing (CISP), vol. 2 (2010)
23.
Gordon, R., Rangayan, R.M.: Feature enhancement of Film mammograms using fixed and adaptive Neighborhoods. Appl. Opt. 23, 560–564 (1984)CrossRef
24.
Hassanien, A., Badr, A.: A comparative study on digital mammography enhancement algorithms based on fuzzy theory. Stud. Inf. Contr. 12, 21–31 (2003)
25.
Davies, E.: Machine Vision: Theory, Algorithms and Practicalities, pp. 26–27, 79–99. Academic Press, New York (1990)
26.
27.
Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991)CrossRef
28.
Sittig, D.F., Wright, A., Osheroff, J.A., Middleton, B., Teich, J.M., Ash, J.S., Campbell, E., Bates, D.W.: Grand challenges in clinical decision support. J. Biomed. Inf. 41(2), 387–392 (2008)CrossRef
Metadaten
Titel
An Investigation of Machine Learning and Neural Computation Paradigms in the Design of Clinical Decision Support Systems (CDSSs)
verfasst von
Summrina K. Wajid
Amir Hussain
Bin Luo
Kaizhu Huang
Copyright-Jahr
2016
Buch
Advances in Brain Inspired Cognitive Systems

Print ISBN: 978-3-319-49684-9

Electronic ISBN: 978-3-319-49685-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-49685-6_6