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Título:
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Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian neural networks
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Autor/a:
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Arizmendi Pereira, Carlos Julio; Sierra Bueno, Daniel Alfonso; Vellido Alcacena, Alfredo; Romero Merino, Enrique
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
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Abstract:
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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 analysis and illustrates a method that combines Gaussian Decomposition, dimensionality reduction by Moving Window with Variance Analysis and classification using adaptively regularized Artificial Neural Networks. The method yields encouraging results in the task of binary classification of human brain tumours, even for tumour types that have seldom been analyzed from this viewpoint. |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts
-Brain -- Tumors -- Diagnosis
-Expert systems (Computer science)
-Brain tumour diagnosis
-Magnetic resonance spectroscopy
-Moving window and variance analysis
-Bayesian neural networks
-Cervell -- Tumors
-Sistemes experts (Informàtica) |
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Derechos:
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Tipo de documento:
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Artículo - Versión publicada
Artículo |
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