Vijayalakshmi & Rajesh, 2019 Image Dataset

Article Title: Deep learning approach to detect malaria from microscopic images

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Description

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

Demography

• Country: India
• City: Chennai
• Study Area: Tagore Medical College & Hospital, Chennai, India
• Parasite’s species: Plasmodium falciparum
• Number of patients: 50 patients

Imaging Technique

• Optical Train: Microscope with camera
• Microscope: Olympus CX21i bright field microscopic stage
• Camera: Canon EOS1200D digital camera
• Magnification: 100x

Dataset Availability

Corresponding Author	DOI
Vijayalakshmi (vijayalakshmi.av@vit.ac.in)	https://doi.org/10.1007/s11042-019-7162-y

Cite this article

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

Vijayalakshmi A, Rajesh Kanna B Deep learning approach to detect malaria from microscopic images. Multimed Tools Appl 79, 15297–15317 (2020). https://doi.org/10.1007/s11042-019-7162-y