Lung Tumor Motion Prediction by static neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F12%3A00198033" target="_blank" >RIV/68407700:21220/12:00198033 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Lung Tumor Motion Prediction by static neural networks

Original language description

This paper presents a study of lung tumormotion time-series prediction, first, with the use of conventional static (feedforward) MLP neural network (with a single hidden perceptron layer) and, second, with the static quadratic neural unit (QNU), i.e., aclass of polynomial neural network (or a higher-order neural unit). We also demonstrate that QNU can be trained in a very efficient and fast way for real time retraining due to its linear nature of optimization problem. The objective is the prediction accuracy of 1 [mm] for 1-second prediction horizon. So it is well applicable for radiation tracking therapy.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

JB - Sensors, detecting elements, measurement and regulation

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2012

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů