Author ORCID Identifier
0009-0001-1846-0879
Document Type
Conference Paper
Disciplines
Statistics
Abstract
The accurate classification of nanoparticles (NPs) based on their shapes is crucial for understanding their physical-chemical properties and predict their bioactivity. Nowadays, synthesis method are able to produce a broad range of shapes, such as spheres, cubes and branched NPs and commonly these NP shapes are only described qualitative. This study presents NP descriptors obtained from NPs contours extracted from electron microscopy images. Descriptors such as Fourier descriptors, aspect ratio, and compactness are then used as input for machine learning classifiers. In particular, XGBoost, Random Forest, and neural networks are explored and the their performances are compared and discussed.
DOI
https://doi.org/10.21427/5keh-9998
Recommended Citation
Caitlin Robertson, Caitlin and Lopez, Hender, "Shape-based nanoparticle classification using machine learning" (2025). SAML-25 Workshop on Statistical and Machine Learning. 21.
https://arrow.tudublin.ie/saml/21
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025 at TU Dublin, Ireland.
doi:10.21427/5keh-9998