Please use this identifier to cite or link to this item:
https://www.arca.fiocruz.br/handle/icict/19466
THE MALARIA SYSTEM MICROAPP: A NEW, MOBILE DEVICE-BASED TOOL FOR MALARIA DIAGNOSIS
Author
Affilliation
Federal Rural University of Pernambuco. Department of Statistics and Informatics. Recife, PE, Brazil / Federal University of Rio Grande do Norte. Department of Informatics and Applied Mathematics. Natal, RN, Brazil.
Universitat Politècnica de Catalunya. BarcelonaTech. Barcelona, Spain.
Vall dHebron University Hospital. Microbiology Department. Barcelona, Spain.
Vall dHebron University Hospital. Microbiology Department. Barcelona, Spain.
Universitat Politècnica de Catalunya. BarcelonaTech. Barcelona, Spain.
Vall dHebron University Hospital. Microbiology Department. Barcelona, Spain.
Universitat Politècnica de Catalunya. BarcelonaTech. Barcelona, Spain.
Fundação Oswaldo Cruz. Instituto Aggeu Magalhães. Recife, PE, Brasil / Federal University of Pernambuco. Keizo Asami Laboratory of Imunopathology. Recife, PE, Brazil.
Vall dHebron University Hospital. Microbiology Department. Barcelona, Spain.
Federal Rural University of Pernambuco. Department of Statistics and Informatics. Recife, PE, Brazil / Federal University of Pernambuco. Keizo Asami Laboratory of Imunopathology. Recife, PE, Brazil.
Universitat Politècnica de Catalunya. BarcelonaTech. Barcelona, Spain.
Vall dHebron University Hospital. Microbiology Department. Barcelona, Spain.
Vall dHebron University Hospital. Microbiology Department. Barcelona, Spain.
Universitat Politècnica de Catalunya. BarcelonaTech. Barcelona, Spain.
Vall dHebron University Hospital. Microbiology Department. Barcelona, Spain.
Universitat Politècnica de Catalunya. BarcelonaTech. Barcelona, Spain.
Fundação Oswaldo Cruz. Instituto Aggeu Magalhães. Recife, PE, Brasil / Federal University of Pernambuco. Keizo Asami Laboratory of Imunopathology. Recife, PE, Brazil.
Vall dHebron University Hospital. Microbiology Department. Barcelona, Spain.
Federal Rural University of Pernambuco. Department of Statistics and Informatics. Recife, PE, Brazil / Federal University of Pernambuco. Keizo Asami Laboratory of Imunopathology. Recife, PE, Brazil.
Abstract
BACKGROUND:
Malaria 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.
OBJECTIVE:
The 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.
METHODS:
The 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.
RESULTS:
As 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.
CONCLUSIONS:
Accessibility 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.
Share