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Automated detection of Malaria parasites on thick blood smears via mobile devices

 
: Rosado, Luis; Correia da Costa, José M.; Da Costa, J.M.C.; Elias, Dirk; Cardoso, Jaime S.

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Procedia computer science 90 (2016), pp.138-144
ISSN: 1877-0509
International Conference on Medical Imaging Understanding and Analysis (MIUA) <20, 2016, Loughborough>
English
Journal Article, Conference Paper, Electronic Publication
Fraunhofer AICOS ()
image analysis; malaria; Computer Aided Diagnosis; microscopy; mobile device

Abstract
An estimated 214 million cases of malaria were detected in 2015, which caused approximately 438 000 deaths. Around 90% of those cases occurred in Africa, where the lack of access to malaria diagnosis is largely due to shortage of expertise and equipment. Thus, the importance to develop new tools that facilitate the rapid and easy diagnosis of malaria for areas with limited access to healthcare services cannot be overstated. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of P.falciparum trophozoites and white blood cells in Giemsa stained thick blood smears. The main differential factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, using a dataset of 194 images manually annotated by an experienced parasilogist.

: http://publica.fraunhofer.de/documents/N-422696.html