Dantas Oliveira et al., 2018 Image Dataset
Article Title: Analysis of Convolutional Neural Networks and Shape Features for Detection and Identification of Malaria Parasites on Thin Blood Smears
Article Link: ref
Description
This is a thin, Giemsa-stained blood smear image dataset
Demography
- Country: Spain
- City: Barcelona
- Study Location: Microbiology Department (Drassanes Unit) of Vall d’Hebron Hospital, Barcelona
- Parasite’s species: Plasmodium falciparum
Imaging Technique
- Optical Train: Camera attached to a microscope
- Microscope Type: Light Microscope
- Camera Type: Nikon E5400 camera
- Magnification: 100× with oil immersion
- Image Resolution: 640 × 480 pixels
- Number of Images: 50 images
Dataset Availability
| Corresponding Author | DOI |
|---|---|
| Bruno M. Carvalho (bruno@dimap.ufrn.br) | https://doi.org/10.1007/978-3-319-75193-1_23 |
Cite this Article
❗🛑 If you are using this resource, please cite:
Dantas Oliveira, A. et al. (2018). An Automatic System for Computing Malaria Parasite Density in Thin Blood Films. In: Mendoza, M., Velastín, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2017. Lecture Notes in Computer Science(), vol 10657. Springer, Cham. https://doi.org/10.1007/978-3-319-75193-1_23