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Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian neural networks

Author
Arizmendi, C.; Sierra, D.A.; Vellido, A.; Romero, E.
Type of activity
Journal article
Journal
Expert systems with applications
Date of publication
2014-09
Volume
41
Number
11
First page
5296
Last page
5307
DOI
https://doi.org/10.1016/j.eswa.2014.02.031 Open in new window
Project funding
Adquisición de conocimiento en farmacoproteomica mediante métodos avanzados de inteligencia artificial (KAPPA AIM)
Artificial Intelligence-based decision tools for decision making support in oncology
Repository
http://hdl.handle.net/2117/82964 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S0957417414001079 Open in new window
Abstract
Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, which are the source of valuable data in the form of signals and images. Expert radiologists should benefit from their use as part of an at least partially automated computer-based medical decision support system. This paper focuses on Magnetic Resonance Spectroscopy signal analy...
Citation
Arizmendi, C., Sierra, D.A., Vellido, A., Romero, E. Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian neural networks. "Expert systems with applications", Setembre 2014, vol. 41, núm. 11, p. 5296-5307.
Keywords
Bayesian neural networks, Brain tumour diagnosis, Magnetic resonance spectroscopy, Moving window and variance analysis
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants