Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning
The proposed method can assist laboratory technicians, particularly in resource-limited healthcare settings. Furthermore, the findings support the potential deployment of this AI-based tool for automated screening in both medical and veterinary applications.
Authors
- Santhad Chuwongin (ORCID: https://orcid.org/0000-0001-7064-9911)
- Veerayuth Kittichai (ORCID: https://orcid.org/0000-0003-4320-5922)
- Teerawat Tongloy (ORCID: https://orcid.org/0009-0001-0670-3124)
- Morakot Kaewthamasorn (ORCID: https://orcid.org/0000-0003-3072-8708)
- Siridech Boonsang (ORCID: https://orcid.org/0000-0003-2866-8902)
- Naruchit Soiphet
Institutions
- Chulalongkorn University (TH)
- King Mongkut's Institute of Technology Ladkrabang (TH)
Publication Details
- Journal
- Parasites & Vectors
- Published
- 2026-02-09
- DOI
- https://doi.org/10.1186/s13071-026-07257-9
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00