Document Type
Article
Publication Date
3-2026
Abstract
Manual morphological analysis of actinide particles from scanning electron microscope (SEM) imagery is a critical component of nuclear forensics but is prone to significant inter-analyst variability. To address this challenge, this work develops and evaluates an automated classification method using deep learning. We introduce a methodology based on partitioning 1906 SEM images, representing 13 classes of uranium compounds, into smaller patches for analysis. Three convolutional neural network (CNN) architectures of increasing complexity were compared: a custom baseline CNN, a simple transfer learning model using ResNet50v1, and a complex model featuring hierarchical feature extraction and a spatial attention mechanism built upon ResNet50v2. The final image classification was determined using a patch-based, class-balanced voting system. The complex model achieved a superior balanced accuracy of 94% on the test set, significantly outperforming the simple transfer learning model (89%) and the baseline model (77%). These results demonstrate that a sophisticated transfer learning architecture can serve as a robust, objective tool to increase the accuracy and consistency of actinide particle classification, thereby enhancing nuclear forensic capabilities.
Source Publication
Journal of Chemometrics (ISSN 0886-9383 | eISSN 1099-128X)
Recommended Citation
N. A.Petrocelli, L. C.Lambert, B. J.Borghetti, and A. A.Bickley, “Transfer Learning Neural Networks for Nuclear Forensic Image Morphology Using Image Splitting Techniques,” Journal of Chemometrics40, no. 3 (2026): e70112, https://doi.org/10.1002/cem.70112.
Comments
© 2026 The Authors. Journal of Chemometrics published by John Wiley & Sons Ltd.
The full article is made available on AFIT Scholar as the repository of the authors' employer. Shared in accordance with the CC-BY-NC license.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.