Zhong et al., 2023 Image Dataset

Article Title: Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy

Article Link: ref

Description

It is a thick, Giemsa-stained blood smear image dataset.

Demography

• Country: Sudan
• City: White Nile
• Study Area: Alkawa Hospital, Alkawa, White Nile State, Sudan
• Parasite’s species: Plasmodium falciparum, Plasmodium vivax

Imaging Technique

• Optical Train: Microscope + smartphone
• Microscope Type: Mobile Microscope Model
• Magnification: 100×
• Number of Images: 111 images

Dataset Availability

Corresponding Author	DOI
Seedahmed S. Mahmoud (mahmoud@stu.edu.cn)	10.1109/OJEMB.2023.3328435

Cite this Article

❗🛑 If you are using this resource, please cite:

Zhong, Y., Dan, Y., Cai, Y., Lin, J., Huang, X., Mahmoud, O., Hald, E. S., Kumar, A., Fang, Q., & Mahmoud, S. S. (2023). Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy. IEEE Open Journal of Engineering in Medicine and Biology, 4, 226–233. https://doi.org/10.1109/OJEMB.2023.3328435