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Deep learning evaluation demonstrates automated fission track cluster segmentation in microscope imagery, highlighting potential applications for nuclear forensics.
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.
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Elgad et al. (2024) studied this question.
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