Comparison of machine learning approaches for radioisotope identification using NaI(TI) gamma-ray spectrum

Abstract

This research aims at comparing the performance of different machine learning algorithms used for NaI(TI) gamma-ray detector based radioisotope identification. Six machine learning algorithms were implemented, including support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), naive Bayes (NB), decision tree (DT), and multilayer perceptron (MLP). The hyper-parameters of each model were elaborately optimized. The effects of data size, statistical fluctuation, and spectrum drift were considered. Results show that for smaller data size (5 types of radioisotopes and 6000 spectra), the support vector machine and the logistic regression classifier exhibit higher identification accuracy with less training/predicting time. Whereas for larger data size (14 types of radioisotopes corresponding to the standard IEC 62327–2017), the multilayer perceptron showed highest accuracy but required the longest time for model training. The naive Bayes classifier and the decision tree were prone to make mistakes when fluctuations and distortions were added to the spectra. The k-nearest neighbor classifier, though showing high accuracy for most data sets, consumed the longest time while making prediction.