A Clinically Grounded Review of Medical Image Classification: Quantitative Insights into CNNs, Vision Transformers, and Hybrid CNN-ViT Models.
- DOI
- 10.1007/s10278-026-02249-8
- Published
- 2026 Sep 3
- Container
- Journal of imaging informatics in medicine
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s10278-026-02249-8,
title = {A Clinically Grounded Review of Medical Image Classification: Quantitative Insights into CNNs, Vision Transformers, and Hybrid CNN-ViT Models.},
author = {Hussaini H and Bano S and Elyan E and Moreno-Garcia CF},
year = {2026},
journal = {Journal of imaging informatics in medicine},
doi = {10.1007/s10278-026-02249-8},
url = {https://doi.org/10.1007/s10278-026-02249-8}
}RIS
TY - JOUR TI - A Clinically Grounded Review of Medical Image Classification: Quantitative Insights into CNNs, Vision Transformers, and Hybrid CNN-ViT Models. AU - Hussaini H AU - Bano S AU - Elyan E AU - Moreno-Garcia CF PY - 2026 JO - Journal of imaging informatics in medicine DO - 10.1007/s10278-026-02249-8 UR - https://doi.org/10.1007/s10278-026-02249-8 ER -
APA
H, H., S, B., E, E., & CF, M. (2026). A Clinically Grounded Review of Medical Image Classification: Quantitative Insights into CNNs, Vision Transformers, and Hybrid CNN-ViT Models.. Journal of imaging informatics in medicine. https://doi.org/10.1007/s10278-026-02249-8
Source records
- pubmed · retrieved 2026-09-26T02:41:30.306Z