Automatic detection of anomalous thermoluminescent dosimeter glow curves using machine learning. (October 2018)

• Record Type: Journal Article
• Authors: Amit, Gal; Datz, Hanan
• Is Part Of: Radiation measurements. Volume 117(2018:Oct.)
• Page Start: 80
• Page End: 85
• Publication Date: 2018-10
• Subjects: Anomaly detection -- Glow curve -- Machine learning -- Thermoluminescent dosimetry; Nuclear emulsions -- Periodicals; Particle tracks (Nuclear physics) -- Periodicals; Thermoluminescence -- Periodicals; Cosmic rays -- Periodicals; Radiation -- Measurement -- Periodicals; Radiometry -- Periodicals; Radiation Monitoring -- Periodicals; Émulsions nucléaires -- Périodiques; Particules (Physique nucléaire) -- Traces -- Périodiques; Thermoluminescence -- Périodiques; Rayonnement cosmique -- Périodiques; Radiométrie -- Périodiques; 539.77
• DOI: 10.1016/j.radmeas.2018.07.014
• Languages: English
• ISSNs: 1350-4487

Abstract

Abstract: Computerized Glow Curve Analysis (CGCA) has been, and still is, an intensively-investigated subject for the past two decades. CGCA has applied different methods for various applications, from glow curve deconvolution into isolated peaks, through semi-automatic software tools for detecting outliers, to software that discovers exceptional curves by using predefined rules. The method presented herein addresses the subject using a new approach in which a completely automatic algorithm is used for the accurate detection of anomalies in thermoluminescent dosimeter (TLD) glow curves. A Support Vector Machines (SVM) technique, which is a machine learning classification algorithm, is used for the first time for radiation dosimetry applications. The algorithm classifies glow curves into two categories: an acceptable i.e. 'regular' curve, or a curve that exhibits any kind of anomaly i.e. an 'anomalous' curve. The classification method treats the glow curves raw data as a large ensemble of statistical data, and tries to identify exceptional glow curve shapes by statistical means. This classification method is performed in three steps. First, a library of glow curves is manually classified by a human user of the system into the above two classes. Then an iterative training algorithm is applied to these glow curves. The final stage applies a method of comparison between an unidentified glow curve and these two pre-classified sets, and assesses a classification probability to each of the two classes. The results show between 96.2% and 97.7% accuracy of the correct classification to either one of the classes, depending on the admissible false negatives rate. Highlights: A computerized anomaly detection of TLD glow curves is presented. A machine learning approach is originally used for radiation dosimetry protection. Ionizing radiation estimation process is automated using Support Vector Machines. Results show a performance greater than 96% of the algorithm.