National Reference Center Image Dataset
Article Title: Real-life evaluation of deep learning models trained on two datasets for Plasmodium falciparum detection with thin blood smear images at 500x magnification
Article Link: ref
Description
This is a thin, Giemsa-stained blood smear image dataset
Demography
- Country: France
- Study Location: The parasitology lab of the Piti´e-Salpˆetri`ere Malaria NRC (National Reference Center) and the hematology lab of Saint Antoine Hospital
- Number of Patients: 202
- Uninfected - 101
- Infected - 101
- Parasite's Species: Plasmodium falciparum
Imaging Technique
- Optical Train: A smartphone camera attached to an optical microscope with RGB JPG format.
- Microscope Type: Olympus BX51 optical microscope
- Smartphone Type: iPhone 7 and a Google Pixel 6
- Magnification: 500×
- Number of Images: 1250 images
Dataset Availability
| Corresponding Author | DOI |
|---|---|
| A. Acherar ( aniss.acherar@gmail.com) | https://doi.org/10.1016/j.imu.2022.101132 |
Cite this Article
❗🛑 If you are using this resource, please cite:
Molina A, Alférez S, Boldú L, Acevedo A, Rodellar J, Merino A. Sequential classification system for recognition of malaria infection using peripheral blood cell images. J Clin Pathol. 2020 Oct;73(10):665-670. doi: 10.1136/jclinpath-2019-206419. Epub 2020 Mar 16. PMID: 32179558.