Deep Learning for Snake Species Identification to Support Snakebite Treatment: A Comparative Study of Efficient CNN and Vision Transformer Architectures.
- DOI
- 10.3390/ani16203298
- Published
- 2026 Oct 20
- Container
- Animals : an open access journal from MDPI
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/ani16203298,
title = {Deep Learning for Snake Species Identification to Support Snakebite Treatment: A Comparative Study of Efficient CNN and Vision Transformer Architectures.},
author = {Theera-Ampornpunt N and Treepong P and Buaphet P},
year = {2026},
journal = {Animals : an open access journal from MDPI},
doi = {10.3390/ani16203298},
url = {https://doi.org/10.3390/ani16203298}
}RIS
TY - JOUR TI - Deep Learning for Snake Species Identification to Support Snakebite Treatment: A Comparative Study of Efficient CNN and Vision Transformer Architectures. AU - Theera-Ampornpunt N AU - Treepong P AU - Buaphet P PY - 2026 JO - Animals : an open access journal from MDPI DO - 10.3390/ani16203298 UR - https://doi.org/10.3390/ani16203298 ER -
APA
N, T., P, T., & P, B. (2026). Deep Learning for Snake Species Identification to Support Snakebite Treatment: A Comparative Study of Efficient CNN and Vision Transformer Architectures.. Animals : an open access journal from MDPI. https://doi.org/10.3390/ani16203298
Source records
- pubmed · retrieved 2026-11-10T09:07:11.799Z