Comparing Deep Learning Architectures On Gamma-Spectroscopy Analysis For Nuclear Waste Characterization

Authors

DOI:

https://doi.org/10.15392/bjrs.v9i1A.1257

Keywords:

gamma-spectroscopy analysis, deep learning,

Abstract

Neural networks, particularly deep neural networks, are used nowadays with great success in several tasks, such as image classification, image segmentation, translation, text to speech, speech to text, achieving super-human performance. In this study, we explore the capabilities of deep learning on a new field: gamma-spectroscopy analysis, comparing the classification performance of different deep neural network architectures. We choose VGG-16, VGG-19, Xception, ResNet, InceptionV3, and MobileNet architectures, which are available through the Keras Deep Learning framework to identify several different radionuclides (Am-241, Ba133, Cd-109, Co-60, Cs-137, Eu-152, Mn-54, Na-24, and Pb-210). Using an HPGe detector to acquire several gamma spectra from different sealed sources to create a dataset used for the training and validation of the comparison of the neural network. This study demonstrates the strengths and weaknesses of applying deep learning on gamma-spectroscopy analysis for nuclear waste characterization.

Downloads

Download data is not yet available.

References

W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bull. Math. Biophys., 1943.

W. C. Contributors, “Neuron Hand-tuned.” [Online]. Available: https://commons.wikimedia.org/w/index.php?title=File:Neuron_Hand-tuned.svg&oldid=347708343.

R. Raina, A. Madhavan, and A. Y. Ng, “Large-scale deep unsupervised learning using graphics processors,” 2009.

D. Cireșan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images,” in NIPS, 2012.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015.

P. E. Keller and R. T. Kouzes, “Gamma spectral analysis via neural networks,” Proc. 1994 IEEE Nucl. Sci. Symp. - NSS’94, 1995.

P. E. Keller, L. J. Kangas, G. L. Troyer, S. Hashem, and R. T. Kouzes, “Nuclear Spectral Analysis via Artificial Neural Networks for Waste Handling,” IEEE Trans. Nucl. Sci., vol. 42, no. 4, pp. 709–715, 1995.

F. Rosenblatt, “The perceptron: A probabilistic model for information storage and organization in the brain,” Psychol. Rev., 1958.

G. H. Yann LeCun, Yoshua Bengio, “Deep learning,” Nat. Methods, 2015.

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 1800–1807, 2017.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” pp. 1–14, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016.

A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2017.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 2818–2826, 2016.

Downloads

Published

2021-04-30

How to Cite

Gomes Lamas Otero, A., Potiens Junior, A., & Takehiro Marumo, J. (2021). Comparing Deep Learning Architectures On Gamma-Spectroscopy Analysis For Nuclear Waste Characterization. Brazilian Journal of Radiation Sciences, 9(1A). https://doi.org/10.15392/bjrs.v9i1A.1257

Issue

Section

The Meeting on Nuclear Applications (ENAN) 2019