Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning

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Das D., Vongpromek R., Assawariyathipat T., Srinamon K., Kennon K., Stepniewska K., Ghose A., Sayeed A.A., Faiz M.A., Netto R.L.A., Siqueira A., Yerbanga S.R., Ouédraogo J.B., Callery J.J., Peto T.J., Tripura R., Koukouikila-Koussounda F., Ntoumi F., Ong’echa J.M., Ogutu B., Ghimire P., Marfurt J., Ley B., Seck A., Ndiaye M., Moodley B., Sun L.M., Archasuksan L., Proux S., Nsobya S.L., Rosenthal P.J., Horning M.P., McGuire S.K., Mehanian C., Burkot S., Delahunt C.B., Bachman C., Price R.N., Dondorp A.M., Chappuis F., Guérin P.J., Dhorda M. Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malaria Journal Vol.21 No.1 (2022). doi:10.1186/s12936-022-04146-1 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84861

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