Wang et al., 2023 Image Dataset

Article Title: Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears
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Description

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

Demography

• Country: China
• Study Area: Peking Union Medical College Hospital and Yunnan Institute of Parasite Diseases
• Parasite’s species: Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, Plasmodium malariae, Plasmodium knowlesi, Plasmodium cynomolgi

Imaging Technique

• Optical Train: Microscope with camera
• Microscope: Olympus CX31 microscope
• Camera: camera (ACA1920; Basler)
• Image Resolution: 1920 × 1200
• Number of Images: 12708 images
  • P. falciparum - 4405 images
  • P. ovale - 117 images
  • P. malariae - 225 images
  • P. vivax - 3030 images
  • P. knowlesi - 4630 images
  • P. cynomolgi - 139 images

Dataset Availability

Not publicly available to be shared

Cite this article

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

Geng Wang, Guoju Luo, Heqing Lian, Lei Chen, Wei Wu, Hui Liu, Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears, Open Forum Infectious Diseases, Volume 10, Issue 11, November 2023, ofad469, https://doi.org/10.1093/ofid/ofad469