Malaria Research Centre, UNIMAS, Sarawak Image Dataset
Article Title: An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Article Link: ref
Description
This is a thin, Giemsa-stained blood smear image dataset
Demography
- Country: Malaysia
- Study Area: Malaria Research Centre, UNIMAS, Sarawak (MRC-UNIMAS)
- Parasite’s species: Plasmodium vivax, Plasmodium falciparum, Plasmodium ovale, Plasmodium malariae, Plasmodium knowlesi
Imaging Technique
- Optical Train: Microscope + Camera
- Microscope Type: model BX53; Olympus Corp, Tokyo, Japan
- Magnification: 1000×
- Number of Images: 472
Dataset Availability
| Corresponding Author | DOI |
|---|---|
| Available on Request from Malaria Research Centre, UNIMAS | 10.1186/s13071-024-06215-7 |
Cite this Article
❗🛑 If you are using this resource, please cite:
Sukumarran, D., Hasikin, K., Khairuddin, A.S.M. et al. An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images. Parasites Vectors 17, 188 (2024). https://doi.org/10.1186/s13071-024-06215-7