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

Autor
Arizmendi, C.; Sierra, D.A.; Vellido, A.; Romero, E.
Tipus d'activitat
Article en revista
Revista
Expert systems with applications
Data de publicació
2014-09
Volum
41
Número
11
Pàgina inicial
5296
Pàgina final
5307
DOI
https://doi.org/10.1016/j.eswa.2014.02.031 Obrir en finestra nova
Projecte finançador
AIDTUMOUR: HERRAMIENTAS BASADAS EN METODOS DE INTELIGENCIA ARTIFICIAL PARA EL APOYO A LA DECISION EN
Adquisición de conocimiento en farmacoproteomica mediante métodos avanzados de inteligencia artificial (KAPPA AIM)
Repositori
http://hdl.handle.net/2117/82964 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0957417414001079 Obrir en finestra nova
Resum
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...
Citació
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.
Paraules clau
Bayesian neural networks, Brain tumour diagnosis, Magnetic resonance spectroscopy, Moving window and variance analysis
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants