Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set

BACKGROUND: Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibi...

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Detaylı Bibliyografya
Yayımlandı:Malar J
Asıl Yazarlar: Horning, Matthew P., Delahunt, Charles B., Bachman, Christine M., Luchavez, Jennifer, Luna, Christian, Hu, Liming, Jaiswal, Mayoore S., Thompson, Clay M., Kulhare, Sourabh, Janko, Samantha, Wilson, Benjamin K., Ostbye, Travis, Mehanian, Martha, Gebrehiwot, Roman, Yun, Grace, Bell, David, Proux, Stephane, Carter, Jane Y., Oyibo, Wellington, Gamboa, Dionicia, Dhorda, Mehul, Vongpromek, Ranitha, Chiodini, Peter L., Ogutu, Bernhards, Long, Earl G., Tun, Kyaw, Burkot, Thomas R., Lilley, Ken, Mehanian, Courosh
Materyal Türü: Artigo
Dil:Inglês
Baskı/Yayın Bilgisi: BioMed Central 2021
Konular:
Research
Online Erişim:https://ncbi.nlm.nih.gov/pmc/articles/PMC7905596/
https://ncbi.nlm.nih.gov/pubmed/33632222
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12936-021-03631-3
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