The effect of training data size on real-time respiration prediction using long short-term memory model

The effect of training data size on real-time respiration prediction using long short-term memory model

Abstract Aim To investigate the optimal training dataset size (TDS) for respiration prediction accuracy using a long short-term memory (LSTM) model. Methods The respiratory signals of 151 patients acquired with the real-time position management system were retrospectively included in this study. Amo...

Full description

Saved in:
Bibliographic Details
Main Authors: Wenzheng Sun, Jun Dang, Lei Zhang, Qichun Wei, Chao Li, Ye Liu, Huang Jing, Kanghua Huang, Yuanpeng Zhang, Bing Li
Format: Article
Language:English
Published: BMC 2025-06-01
Series:Radiation Oncology
Subjects:
Radiation therapy
Respiratory motion management
Respiration prediction
Training dataset size
Long short-term memory model
Online Access:https://doi.org/10.1186/s13014-025-02676-x
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s13014-025-02676-x

Similar Items