Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Adaptive discriminant analysis for microarray-based classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
Constructing prediction intervals for neural network metamodels of complex systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Automated nonlinear feature generation and classification of foot pressure lesions
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Incorporating Nonlinear Relationships in Microarray Missing Value Imputation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Confidence interval prediction for neural network models
IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
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Information processing of radiotherapy systems has become an important research area for sophisticated radiation treatment methodology. Geometrically precise delivery of radiotherapy in the thorax and upper abdomen is compromised by respiratory motion during treatment. Accurate prediction of the respiratory motion would be beneficial for improving tumor targeting. However, a wide variety of breathing patterns can make it difficult to predict the breathing motion with explicit models. We proposed a respiratory motion predictor, that is, customized prediction with multiple patient interactions using neural network (CNN). For the preprocedure of prediction for individual patient, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the intraprocedure, the proposed CNN used neural networks (NN) for a part of the prediction and the extended Kalman filter (EKF) for a part of the correction. The prediction accuracy of the proposed method was investigated with a variety of prediction time horizons using normalized root mean squared error (NRMSE) values in comparison with the alternate recurrent neural network (RNN). We have also evaluated the prediction accuracy using the marginal value that can be used as the reference value to judge how many signals lie outside the confidence level. The experimental results showed that the proposed CNN can outperform RNN with respect to the prediction accuracy with an improvement of 50%.