Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study.

ABSTRACT:

Background

In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven deep artificial neural networks to propose an accurate and reliable prediction model.

Methods

Seven deep predictor models are trained and tested with 800 breathing signals. In this regard, a nonsequential-correlated hyperparameter optimization algorithm is developed to find the best configuration of parameters for all models. The root mean square error (RMSE), mean absolute error, normalized RMSE, and statistical F-test are also used to evaluate network performance.

Results

Overall, tuning the hyperparameters results in a 25%-30% improvement for

SUBMITTER: Samadi Miandoab P 

PROVIDER: S-EPMC10018664 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature