Sharma et al., 2022 Image Dataset

Article Title: Automatic Detection of Malaria Infected Erythrocytes Based on the Concavity Point Identification and Pseudo-Valley Based Thresholding

Article Link: ref

Description

This is a thin, Leishman-stained blood smear image dataset

Demography

• Country: India
• City: Assam
• Study Area: Pathological clinic of Cachar district, Assam, India
• Parasite’s species: Plasmodium falciparum, Plasmodium vivax
• Number of patients: 200 patients

Imaging Technique

• Magnification: 100×
• Image Resolution: 1500×1000
• Number of Images: 2800 images

Dataset Availability

Corresponding Author	DOI
Rabul Hussain Laskar (rhlaskar@ece.nits.ac.in)	https://doi.org/10.1080/03772063.2020.1787238

Cite this Article

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

Sharma, M., Devi, S. S., & Laskar, R. H. (2022). Automatic Detection of Malaria Infected Erythrocytes Based on the Concavity Point Identification and Pseudo-Valley Based Thresholding. IETE Journal of Research, 68(6), 4043–4060. https://doi.org/10.1080/03772063.2020.1787238