A proposal for automatic diagnosis of malaria

Oliveira, Allisson Dantas; Cabral, Giordano; López, Daniel; Firmo, Caetano; Serrat, Francesc Zarzuela; Albuquerque, Jones

doi:10.1145/2487788.2488022

Cited by 1 publication

(1 citation statement)

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“…Currently, the highest level of accuracy was 91%. Based on these findings, the tool is feasible [ 12 , 13 ] and with new sets of positive images, higher rankings with respect to specificity and sensitivity are possible. In the future, variations and adaptations to the Viola and Jones [ 10 ] method may be enable detection of other species and stages of malaria development.…”

Section: Discussionmentioning

confidence: 99%

The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis

Oliveira¹,

Prats²,

Espasa³

et al. 2017

JMIR Res Protoc

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BackgroundMalaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority.ObjectiveThe objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development.MethodsThe system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells.ResultsAs a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly.ConclusionsAccessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment.

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Section: Discussionmentioning

confidence: 99%