Peer-reviewedCC BYAnimals : an open access journal from MDPI
Deep Learning for Snake Species Identification to Support Snakebite Treatment: A Comparative Study of Efficient CNN and Vision Transformer Architectures
Related
- Comparative and explainable deep learning for cherry (Prunus avium L.) cultivar classification using convolutional neural networks (CNN) and transformer architectures
- Transformer-based architectures for anomaly detection in solar-wind integrated energy infrastructures
- Transformer-inspired architectures make machine-learned electron densities accurate and transferable enough to bypass self-consistency
- Assessment of Convolutional Neural Network and Vision Transformer Architectures for Structural Surface Classification.
- STATUS: Closed. Research Package: Mitigation of Cross-Subspace Semantic Bleed in High-Dimensional Transformer and Latent Diffusion Architectures
BibTeX
@article{anon2026deep,
title = {{Deep Learning for Snake Species Identification to Support Snakebite Treatment: A Comparative Study of Efficient CNN and Vision Transformer Architectures}},
journal = {Animals : an open access journal from MDPI},
year = {2026},
month = oct,
url = {https://europepmc.org/article/PMC/PMC13700715},
note = {CC BY},
}
RIS
TY - JOUR
TI - Deep Learning for Snake Species Identification to Support Snakebite Treatment: A Comparative Study of Efficient CNN and Vision Transformer Architectures
PY - 2026
DA - 2026/10/01
JO - Animals : an open access journal from MDPI
UR - https://europepmc.org/article/PMC/PMC13700715
SN - 2076-2615
ER -