Manescu et al., 2020 Blood Smear Image Dataset
Article Title: Expert-level automated malaria diagnosis on routine blood films with deep neural networks
Article Link : ref
Description
It is a thick, Giemsa-stained blood smear image dataset.
Demography
- Country: Nigeria
- City: Ibadan
- Study Area: University College Hospital (UCH)
- Number of patients: 299 patients
- Parasite’s species: Plasmodium falciparum.
Imaging Technique
- Optical Train: Microscope
- Microscope: Olympus B×63 Brightfield microscope
- Magnification: 100×
Image Preview
Infected
Uninfected
Dataset Availability
| Status | Dataset Link | DOI |
|---|---|---|
| Publicly available | Dataset | 10.1002/ajh.25827 |
Cite this article
❗🛑 If you are using this resource, please cite:
Manescu P, Shaw MJ, Elmi M, Neary-Zajiczek L, Claveau R, Pawar V, Kokkinos I, Oyinloye G, Bendkowski C, Oladejo OA, Oladejo BF, Clark T, Timm D, Shawe-Taylor J, Srinivasan MA, Lagunju I, Sodeinde O, Brown BJ, Fernandez-Reyes D. Expert-level automated malaria diagnosis on routine blood films with deep neural networks. Am J Hematol. 2020 Aug;95(8):883-891. doi: 10.1002/ajh.25827. Epub 2020 Apr 30. PMID: 32282969.