The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis
Open accessAbstract
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.
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AI applications include diagnosing skin cancer from photographs and diagnosing malaria by analyzing peripheral blood samples.
“Applications include diagnosing skin cancer from photographic images and analyzing peripheral blood samples to diagnose malaria (7);”
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- Authors
- Oliveira, A. D., Prats, C., Espasa, M., Zarzuela Serrat, F., Montañola Sales, C., Silgado, A., Codina, D. L., Arruda, M. E., i Prat, J. G., & Albuquerque, J.
- Affiliations
- Universidade Federal do Rio Grande do Norte (BR)
- Universitat Politècnica de Catalunya (ES)
- Vall d'Hebron Hospital Universitari (ES)
- Universidade Federal de Pernambuco (BR)
- Corresponding author
- Oliveira, Allisson Dantas
- ORCID
- Venue
- JMIR Research Protocols
- Publisher
- JMIR Publications Inc.
- Published
- 2017-04-25
- Volume
- 6
- Issue
- 4
- Pages
- e70
- ISSN
- 1929-0748 (ISSN-L)
- PMC
- PMC5424126
- OpenAlex
- W2609362655
- Cited by
- 144
- References
- 17
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 9 · 2023: 11 · 2022: 15 · 2021: 66 · 2020: 21 · 2019: 7 · 2018: 5 · 2017: 1 · 2016: 1
- FWCI
- 3.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Research Protocols (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion, cc-by-nc-nd)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (acceptedVersion, other-oa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- Americanae (AECID Library) (submittedVersion, other-oa)
- Arca - Repositório Institucional da Fiocruz (submittedVersion, other-oa)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Greater South Information System
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (APQ-1037-1.03/08, 2014‐0, -0 and, 465614/2014- 0)
- Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (465614/2014-0, APQ-1037-1.03/08)
- Instituto Nacional de Ciência e Tecnologia para Engenharia de Software (465614/2014-0)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Malaria Research and Control
- Physical Sciences › Engineering › Biomedical Engineering › Biosensors and Analytical Detection
- Keywords
- malaria diagnosis, Plasmodium falciparum, ring-stage parasites, Giemsa-stained blood smears, automated parasite detection, mobile health, low-cost diagnostic device, integral image, Haar-like features, AdaBoost, face detection, resource-limited settings
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