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Counting malaria parasites with a two-stage EM based algorithm using crowsourced data

By Margarita Cabrera Bean, Alba Pagés Zamora, Carles Díaz Vilor, María Postigo Camps, Daniel Cuadrado Sánchez and Miguel Ángel Luengo Oroz


Abstract: Malaria eradication of the worldwide is currently one of the main WHO's global goals. In this work, we focus on the use of human-machine interaction strategies for low-cost fast reliable malaria diagnostic based on a crowdsourced approach. The addressed technical problem consists in detecting spots in images even under very harsh conditions when positive objects are very similar to some artifacts. The clicks or tags delivered by several annotators labeling an image are modeled as a robust finite mixture, and techniques based on the Expectation-Maximization (EM) algorithm are proposed for accurately counting malaria parasites on thick blood smears obtained by microscopic Giemsa-stained techniques. This approach outperforms other traditional methods as it is shown through experimentation with real data

Topics: Medicina, Telecomunicaciones
Publisher: 'Institute of Electrical and Electronics Engineers (IEEE)'
Year: 2017
DOI identifier: 10.1109/EMBC.2017.8037311
OAI identifier:

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