Abstract
This study introduces a novel methodology for the detection and classification of fission track (FT) clusters in microscope images, employing state-of-the-art deep learning techniques for segmentation and classification (Elgad in nuclear forensics—fission track analysis—star segmentation and classification using deep learning, Ben-Gurion University, 2022). The U-Net model, a fully convolutional network, was used to carry out the segmentation of various star-like patterns in both single-class and multi-class scenarios.
| Original language | English |
|---|---|
| Pages (from-to) | 2321-2337 |
| Number of pages | 17 |
| Journal | Journal of Radioanalytical and Nuclear Chemistry |
| Volume | 333 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2024 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Computer vision
- Fission track analysis
- Holmeland security
- Nuclear forensics
- Safeguards investigations
- U-Net
ASJC Scopus subject areas
- Analytical Chemistry
- Nuclear Energy and Engineering
- Radiology Nuclear Medicine and imaging
- Pollution
- Spectroscopy
- Public Health, Environmental and Occupational Health
- Health, Toxicology and Mutagenesis
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