Özbilge et al., 2024 Image Dataset
Article Title: Ensembling Object Detection Models for Robust and Reliable Malaria Parasite Detection in Thin Blood Smear Microscopic Images.
Article Link: ref
Description
This is a thin, Giemsa-stained blood smear image dataset
Demography
- Country: Cyprus
- Study Area: Department of Parasitology, Manisa Celal Bayar University, and the Microbiology Laboratory of Near East University Hospital
- Parasite’s species: Plasmodium vivax, Plasmodium falciparum, Plasmodium ovale
- Number of Patients: 25 patients
- Plasmodium falciparum infected: 21
- Plasmodium vivax infected: 3
- Plasmodium ovale Infected: 1
Imaging Technique
- Optical Train: Microscope with smartphone
- Microscope Type: Light Microscope
- Magnification: 100×
- Image Size: 640 x 640
Dataset Availability
| Corresponding Author | DOI |
|---|---|
| Emre Özbilge (eozbilge@ciu.edu.tr) | 10.1109/ACCESS.2024.3393410 |
Cite this Article
❗🛑 If you are using this resource, please cite:
E. Özbilge, E. Güler and E. Ozbilge, "Ensembling Object Detection Models for Robust and Reliable Malaria Parasite Detection in Thin Blood Smear Microscopic Images," in IEEE Access, vol. 12, pp. 60747-60764, 2024, doi: 10.1109/ACCESS.2024.3393410.