ArticleNanomaterials (Basel, Switzerland)2022
Nanoparticle Detection on SEM Images Using a Neural Network and Semi-Synthetic Training Data.
Article in Nanomaterials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Deep learning for nanoscience scanning electron microscope image classification.Scientific reports · 2026Article
- Vision language models for scientific image analysis: an evaluation highlighting opportunities and challenges.npj computational materials · 2026Article
- A deep learning method for nanoparticle size measurement in SEM images.RSC advances · 2025Article
- Therapeutic Effects of Nanocoating of Apitoxin (Bee Venom) and Polyvinyl Alcohol Supplemented with Zinc Oxide Nanoparticles.Pharmaceutics · 2025Article
- Quantifying Monomer-Dimer Distribution of Nanoparticles from Uncorrelated Optical Images Using Deep Learning.ACS omega · 2025Article
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3 authors.
Funding
Abstract
Processing images represents a necessary step in the process of analysing the information gathered about nanoparticles after characteristic material samples have been scanned with electron microscopy, which often requires the use of image processing techniques or general purpose image manipulation software to carry out tasks such as nanoparticle detection and measurement. In recent years, the use of networks has been successfully implemented to detect and classify electron microscopy images as well as the objects within them. In this work, we present four detection models using two versions of the YOLO neural network architectures trained to detect cubical and quasi-spherical particles in SEM images; the training datasets are a mixture of real images and synthetic ones generated by a semi-arbitrary method. The resulting models were capable of detecting nanoparticles in images different than the ones used for training and identifying them in some cases as the close proximity between nanoparticles proved a challenge for the neural networks in most situations.
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