MalariaApp: um sistema de baixo custo para diagnóstico de malária em lâminas de esfregaço sanguíneo usando dispositivos móveis
Nowadays, a variety of mobile devices are available and accessible to the general population, making it an indispensable item for communication and use of various services. In this same direction, these devices have become quite useful in several areas of expertise, including the medical field. W...
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| Formato: | doctoralThesis |
| Idioma: | pt_BR |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/29654 |
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| Resumo: | Nowadays, a variety of mobile devices are available and accessible to the general population,
making it an indispensable item for communication and use of various services. In this same
direction, these devices have become quite useful in several areas of expertise, including
the medical field. With the integration of these devices and applications, it is possible
to perform preventive work, helping to combat outbreaks and even prevent epidemics.
According to the World Health Organization (2017), malaria is one of the most lethal
infectious diseases in the world, mainly in the region of sub-Saharan Africa, while in Brazil
it is more frequent the occurrence of cases in the Amazon region. For the diagnosis of
malaria it is essential to have trained and experienced technicians to identify the species
and phases of the disease, a crucial part to define the ideal dosages of administering
medication to patients. In this work, we propose a low-cost malaria diagnosis system using
mobile devices, where some segmentation, digital image processing, and convolutional
neural networks techniques are applied to perform cell counting, parasitemia estimation,
and Plasmodium parasite classification in the species P.falciparum and P.vivax in the
trophozoite step. A prototype with 3D parts and electronic automation was proposed to
perform the scanning and imaging of blood slides to integrate with the mobile system and
perform the on-site diagnosis, without the need for changing microscopic equipment, thus,
based on the premise of low cost. A 93% accuracy was obtained in a convolutional neural
network model. In view of this, it is possible to break barriers of accessibility in countries
with few resources in the use of diagnostic tools and screening of diseases. |
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