Journal article
K. Ferji*
Nanoscale, vol. 17, 2025, pp. 18777-18786
Nanoscale, vol. 17, 2025, pp. 18777-18786
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APA
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Ferji*, K. (2025). DetectNano: deep learning detection in TEM images for high-throughput nanostructure characterization. Nanoscale, 17, 18777–18786.
Chicago/Turabian
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Ferji*, K. “DetectNano: Deep Learning Detection in TEM Images for High-Throughput Nanostructure Characterization.” Nanoscale 17 (2025): 18777–18786.
MLA
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Ferji*, K. “DetectNano: Deep Learning Detection in TEM Images for High-Throughput Nanostructure Characterization.” Nanoscale, vol. 17, 2025, pp. 18777–86.
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@article{k2025a,
title = {DetectNano: deep learning detection in TEM images for high-throughput nanostructure characterization.},
year = {2025},
journal = {Nanoscale},
pages = {18777-18786},
volume = {17},
author = {Ferji*, K.}
}
Abstract
The rapid and unbiased characterization of self-assembled polymeric vesicles in transmission electron microscopy (TEM) images remains a challenge in polymer science. Here, we present a deep learning-powered detection framework based on YOLOv8, enhanced with Weighted Box Fusion, to automate the identification and size estimation of polymer nanostructures. By incorporating multiple morphologies in the training dataset, we achieve robust detection across unseen TEM images. Our results demonstrate that the model provides accurate vesicle detection within 2 seconds-an efficiency unattainable using traditional image analysis software. The proposed framework enables reproducible and scalable nano-object characterization, paving the way for a general AI-driven automation in polymer self-assembly research.