National Institute of Health Plasmodium falciparum Blood Smear Image Dataset
Article Title (Thick): Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears
Article Title (Thin): Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
Article Link (Thick): ref
Article Link (Thin): ref
Description
It is a thick and thin, Giemsa-stained blood smear image dataset.
Demography
- Country: Bangladesh
- City: Chittagong
- Study Area: Chittagong Medical College Hospital
- Number of patients: 200 patients
- Parasite’s species: Plasmodium falciparum.
Imaging Technique
- Optical Train: Smartphone appended to a microscope eyepiece
- Image Resolution: 3024 x 4032
- Microscope: Light Microscope
- Magnification: 100×
Image Preview
Thick (Infected)
Thin (Infected)
Thin (Uninfected)
Dataset Availability
| Status | Dataset Link | DOI |
|---|---|---|
| Publicly available (Thick) | Dataset | 10.1109/JBHI.2019.2939121 |
| Publicly available (Thin) | Dataset | 10.7717/peerj.4568 |
Cite this article
❗🛑 If you are using this resource, please cite:
Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, Maude RJ, Jaeger S, Antani S. Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears. IEEE J Biomed Health Inform. 2020 May;24(5):1427-1438. doi: 10.1109/JBHI.2019.2939121. Epub 2019 Sep 23. PMID: 31545747.