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Neuro-genetic non-invasive temperature estimation: intensity and spatial prediction
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Orientador(es)
Resumo(s)
Objectives: The existence of proper non-invasive temperature estimators is an essential aspect when thermal therapy applications are envisaged. These estimators
must be good predictors to enable temperature estimation at different operational situations, providing better control of the therapeutic instrumentation. In this work, radial basis functions artificial neural networks were constructed to access temperature evolution on an ultrasound insonated medium.
Methods: The employed models were radial basis functions neural networks with
external dynamics induced by their inputs. Both the most suited set of model inputs
and number of neurons in the network were found using the multi-objective genetic
algorithm. The neural models were validated in two situations: the operating ones, as
used in the construction of the network; and in 11 unseen situations. The new data
addressed two new spatial locations and a new intensity level, assessing the intensity
and space prediction capacity of the proposed model.
Results: Good performance was obtained during the validation process both in terms
of the spatial points considered and whenever the new intensity level was within the range of applied intensities. A maximum absolute error of 0:5 C 10% (0.5 8C is the
gold-standard threshold in hyperthermia/diathermia) was attained with low computationally complex models.
Conclusion: The results confirm that the proposed neuro-genetic approach enables
foreseeing temperature propagation, in connection to intensity and space parameters,
thus enabling the assessment of different operating situations with proper
temperature resolution.
Descrição
Palavras-chave
Black-box modelling Artificial neural networks Multi-objective genetic algorithms Non-invasive temperature estimation
Contexto Educativo
Citação
Teixeira, César A.; Graça Ruano, M.; Ruano, António E.; Pereira, Wagner C. A. Neuro-genetic non-invasive temperature estimation: Intensity and spatial prediction, Artificial Intelligence in Medicine, 43, 2, 127-139, 2008.