Efficient feature extraction using light-weight CNN attention-based deep learning architectures for ultrasound fetal plane classification
- DOI
- 10.1007/s13246-025-01566-6
- Published
- 2025-05-28
- Container
- Physical and Engineering Sciences in Medicine
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s13246-025-01566-6,
title = {Efficient feature extraction using light-weight CNN attention-based deep learning architectures for ultrasound fetal plane classification},
author = {Arrun Sivasubramanian and Divya Sasidharan and V. Sowmya and Vinayakumar Ravi},
year = {2025},
journal = {Physical and Engineering Sciences in Medicine},
doi = {10.1007/s13246-025-01566-6},
url = {https://doi.org/10.1007/s13246-025-01566-6}
}RIS
TY - JOUR TI - Efficient feature extraction using light-weight CNN attention-based deep learning architectures for ultrasound fetal plane classification AU - Arrun Sivasubramanian AU - Divya Sasidharan AU - V. Sowmya AU - Vinayakumar Ravi PY - 2025 JO - Physical and Engineering Sciences in Medicine DO - 10.1007/s13246-025-01566-6 UR - https://doi.org/10.1007/s13246-025-01566-6 ER -
APA
Sivasubramanian, A., Sasidharan, D., Sowmya, V., & Ravi, V. (2025). Efficient feature extraction using light-weight CNN attention-based deep learning architectures for ultrasound fetal plane classification. Physical and Engineering Sciences in Medicine. https://doi.org/10.1007/s13246-025-01566-6
Source records
- crossref · retrieved 2026-09-25T20:59:01.747Z