IMPROVEMENT of DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

Gal Amit, Hanan Datz

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify 'anomalous' (having any kind of anomaly) GCs for manual review, and 'regular' (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers-regular and weighted ones-using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

Original languageEnglish
Article numberncy185
Pages (from-to)36-43
Number of pages8
JournalRadiation Protection Dosimetry
Volume184
Issue number1
DOIs
StatePublished - 1 Jul 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Fingerprint

Dive into the research topics of 'IMPROVEMENT of DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS'. Together they form a unique fingerprint.

Cite this