Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

Open Access

Peer-reviewed

Research Article

Han Sang Park ,

* E-mail: hp36@duke.edu

Affiliation Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
⨯
Matthew T. Rinehart,

Affiliation Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
⨯
Katelyn A. Walzer,

Affiliations Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, United States of America, Duke Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
⨯
Jen-Tsan Ashley Chi,

Affiliations Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, United States of America, Duke Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
⨯
Adam Wax

Affiliation Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
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Parasitic diseases
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Machine learning algorithms
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Malarial parasites
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Malaria
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Trophozoites
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Red blood cells
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Plasmodium
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Machine learning
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