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Regressive and non-regressive RBFNN estimators in non-invasive temperature estimation
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Orientador(es)
Resumo(s)
In this paper two modelling strategies
were applied for non-invasive temperature
estimation in a gel-based phantom subjected to physiotherapeutic ultrasound. The two strategies differ in the consideration of regressive or nonregressive radial basis functions neural networks (RBFNN) structures. The gel-based phantom was
heated using four different ultrasound intensities.
Temperature was monitored at five points inside the phantom, where temperature is to be estimated. The best regressive model reaches a mean maximum absolute error of 0.4 ºC, against 0.8 ºC presented by the best non-regressive model. The regressive strategy presented better error performance with a
smaller computational complexity. Thus, being the appropriate one for real-time temperature monitoring in hyperthermia/diathermia procedures,
where resolutions below 0.5 ºC are required.
Descrição
Palavras-chave
Non-invasive Temperature Estimation Neural Networks Multi-objective Optimization Therapeutic Ultrasound
Contexto Educativo
Citação
Teixeira, C. A.; Pereira, W. C. A.; Ruano, M. G.; Ruano, A. E. Regressive and non-regressive RBFNN estimators in non-invasive temperature estimation, Trabalho apresentado em 21º Congresso Brasileiro de Engenharia Biomédica, In Actas do 21º Congresso Brasileiro de Engenharia Biomédica, Baía, 2008.