IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS. Issue 1 (29th October 2018)

• Record Type: Journal Article
• Authors: Amit, Gal; Datz, Hanan
• Is Part Of: Radiation protection dosimetry. Volume 184:Issue 1(2019)
• Page Start: 36
• Page End: 43
• Publication Date: 2018-10-29
• Subjects: Radiation dosimetry -- Periodicals; Radiation -- Safety measures -- Periodicals; 363.1799
• DOI: 10.1093/rpd/ncy185
• Languages: English
• ISSNs: 0144-8420

Abstract

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.