Lung motion prediction by static neural networks

Ricardo Rodriguez, Kei Ichiji, Ivo Bukovsky, Jiri Bila, Noriasu Homma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The paper presents a study and comparison of static feedforward neural network performance for prediction of lung motion. A feedforward neural network with local and global optimization to predict human lung respiration is presented. Applicability of the Levenberg-Marquardt algorithm and the backpropagation learning rule during the batch training are discussed. Sliding window learning for retraining static neural network is applied as a more efficient learning prediction method. Prediction results are presented and compared to demonstrate the effectiveness of the applied neural network method.

Original languageEnglish
Title of host publication4th International Symposium on Measurement, Analysis and Modelling of Human Functions 2010, ISHF 2010
Pages40-46
Number of pages7
Publication statusPublished - 2010
Event4th International Symposium on Measurement, Analysis and Modelling of Human Functions 2010, ISHF 2010 - Prague, Czech Republic
Duration: 2010 Jun 142010 Jun 16

Publication series

Name4th International Symposium on Measurement, Analysis and Modelling of Human Functions 2010, ISHF 2010

Other

Other4th International Symposium on Measurement, Analysis and Modelling of Human Functions 2010, ISHF 2010
Country/TerritoryCzech Republic
CityPrague
Period10/6/1410/6/16

Keywords

  • Lung tumor motion
  • Multilayer perceptron
  • Prediction
  • Radiation tracking therapy
  • Sliding window retraining
  • Static feedforward neural networks

ASJC Scopus subject areas

  • Modelling and Simulation

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