Assessment of Expert-Level Automated Detection of Plasmodium falciparum in Digitized Thin Blood Smear Images | Zendy

Po-Chen Kuo | Zendy; HaoYuan Cheng | Zendy; Pi-Fang Chen | Zendy; Yulun Liu | Zendy; Martin Kang | Zendy; Min-Chu Kuo | Zendy; Shih-Fen Hsu | Zendy; Hsin-Jung Lu | Zendy; Stefan Hong | Zendy; Chan-Hung Su | Zendy; DingPing Liu | Zendy; Yi-Chin Tu | Zendy; Jen-Hsiang Chuang | Zendy

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Assessment of Expert-Level Automated Detection of Plasmodium falciparum in Digitized Thin Blood Smear Images

Author(s) -

Po-Chen Kuo,

HaoYuan Cheng,

Pi-Fang Chen,

Yulun Liu,

Martin Kang,

Min-Chu Kuo,

Shih-Fen Hsu,

Hsin-Jung Lu,

Stefan Hong,

Chan-Hung Su,

DingPing Liu,

Yi-Chin Tu,

Jen-Hsiang Chuang

Publication year - 2020

Publication title -

jama network open

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.278

H-Index - 39

ISSN - 2574-3805

DOI - 10.1001/jamanetworkopen.2020.0206

Subject(s) - malaria , blood smear , plasmodium falciparum , artificial intelligence , receiver operating characteristic , benchmark (surveying) , convolutional neural network , computer science , parasitemia , medicine , pattern recognition (psychology) , pathology , machine learning , cartography , geography

This diagnostic study assesses an expert-level detection algorithm for Plasmodium falciparum , a bacteria that causes malaria, using a publicly available benchmark image data set.

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