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Sensors 2017, 17(10), 2167; https://doi.org/10.3390/s17102167

Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination

1
Fraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
2
Instituto Nacional de Saúde Dr. Ricardo Jorge, Rua Alexandre Herculano 321, 4000-055 Porto, Portugal
3
INESC TEC (Institute for Systems and Computer Engineering, Technology and Science) and Department of Electrical and Computer Engineering of the Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Received: 17 August 2017 / Revised: 14 September 2017 / Accepted: 18 September 2017 / Published: 21 September 2017
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Abstract

Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times. View Full-Text
Keywords: image analysis; malaria; computer-aided diagnosis; microscopy; mobile devices image analysis; malaria; computer-aided diagnosis; microscopy; mobile devices
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rosado, L.; da Costa, J.M.C.; Elias, D.; Cardoso, J.S. Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination. Sensors 2017, 17, 2167.

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