Machine Learning Algorithms for Identification of Abnormal Glow Curves and Associated Abnormality in Caso4:Dy-Based Personnel Monitoring Dosimeters.

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ID: 113647
2020
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Abstract
In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorithms. The performance of all algorithms is compared by using various parameters. Results show a fairly good accuracy of 99.05% for the classification of GCs by RF algorithm. Whereas 96.7% and 96.1% accuracy is achieved using ANN and SVM, respectively. The RF-based classifier is recommended for GC classification as well as in assisting the fault determination of the TLD reader system.
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pathan2020machineradiation Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pathan, Munir S;Pradhan, S M;Selvam, T Palani;
Journal Radiation protection dosimetry
Year 2020
DOI ncaa108
URL
Keywords
melanoma classification diagnostics ultrasonography spectrophotometry dermatoscopy melanocytic tumor

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