A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.
- DOI
- 10.1186/s13071-026-07438-6
- Published
- 2026-05-16
- Container
- Parasit Vectors
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1186/s13071-026-07438-6,
title = {A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.},
author = {Siddiqui GA and Bera A and Lhila A and Rajput AS and Nitika N and Bharti PK and Das A and Mahapatra T.},
year = {2026},
journal = {Parasit Vectors},
doi = {10.1186/s13071-026-07438-6},
url = {https://doi.org/10.1186/s13071-026-07438-6}
}RIS
TY - JOUR TI - A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images. AU - Siddiqui GA AU - Bera A AU - Lhila A AU - Rajput AS AU - Nitika N AU - Bharti PK AU - Das A AU - Mahapatra T. PY - 2026 JO - Parasit Vectors DO - 10.1186/s13071-026-07438-6 UR - https://doi.org/10.1186/s13071-026-07438-6 ER -
APA
GA, S., A, B., A, L., AS, R., N, N., PK, B., A, D., & T., M. (2026). A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.. Parasit Vectors. https://doi.org/10.1186/s13071-026-07438-6
Source records
- europe-pmc · retrieved 2026-09-26T13:34:17.646Z