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Transl Res. 2018 Apr;194:36-55. doi: 10.1016/j.trsl.2017.12.004. Epub 2018 Jan 12.

Image analysis and machine learning for detecting malaria.

Author information

1
U.S. National Library of Medicine, National Institutes of Health, Bethesda, Maryland.
2
Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
3
Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts.
4
U.S. National Library of Medicine, National Institutes of Health, Bethesda, Maryland. Electronic address: stefan.jaeger@nih.gov.

Abstract

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.

PMID:
29360430
PMCID:
PMC5840030
[Available on 2019-04-01]
DOI:
10.1016/j.trsl.2017.12.004
[Indexed for MEDLINE]
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