Brain tumour classification using Gaussian decomposition and neural networks

Carlos Arizmendi; Daniel A. Sierra; Alfredo Vellido; Enrique Romero

doi:10.1109/IEMBS.2011.6091366

Brain tumour classification using Gaussian decomposition and neural networks

Carlos Arizmendi, Daniel A. Sierra, Alfredo Vellido, Enrique Romero

Research output: Book / Book Chapter / Report › Research Books › peer-review

3 Scopus citations

Abstract

The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.

Original language	English
Title of host publication	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages	5645-5648
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2011.6091366
State	Published - 2011
Externally published	Yes
Event	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States Duration: 30 Aug 2011 → 3 Sep 2011

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Country/Territory	United States
City	Boston, MA
Period	30/08/11 → 3/09/11

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10.1109/IEMBS.2011.6091366

Cite this

Arizmendi, C., Sierra, D. A., Vellido, A., & Romero, E. (2011). Brain tumour classification using Gaussian decomposition and neural networks. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 (pp. 5645-5648). Article 6091366 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/IEMBS.2011.6091366

Arizmendi, Carlos ; Sierra, Daniel A. ; Vellido, Alfredo et al. / Brain tumour classification using Gaussian decomposition and neural networks. 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011. 2011. pp. 5645-5648 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Brain tumour classification using Gaussian decomposition and neural networks",

abstract = "The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.",

author = "Carlos Arizmendi and Sierra, {Daniel A.} and Alfredo Vellido and Enrique Romero",

year = "2011",

doi = "10.1109/IEMBS.2011.6091366",

language = "Ingl{\'e}s",

isbn = "9781424441211",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

pages = "5645--5648",

booktitle = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011",

note = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 ; Conference date: 30-08-2011 Through 03-09-2011",

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Arizmendi, C, Sierra, DA, Vellido, A & Romero, E 2011, Brain tumour classification using Gaussian decomposition and neural networks. in 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011., 6091366, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 5645-5648, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011, Boston, MA, United States, 30/08/11. https://doi.org/10.1109/IEMBS.2011.6091366

Brain tumour classification using Gaussian decomposition and neural networks. / Arizmendi, Carlos; Sierra, Daniel A.; Vellido, Alfredo et al.
33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011. 2011. p. 5645-5648 6091366 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Book / Book Chapter / Report › Research Books › peer-review

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M3 - Libros de Investigación

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SN - 9781424441211

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

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Arizmendi C, Sierra DA, Vellido A, Romero E. Brain tumour classification using Gaussian decomposition and neural networks. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011. 2011. p. 5645-5648. 6091366. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/IEMBS.2011.6091366

Brain tumour classification using Gaussian decomposition and neural networks

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