Banca de DEFESA: ALLISSON DANTAS DE OLIVEIRA

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : ALLISSON DANTAS DE OLIVEIRA
DATE: 29/11/2019
TIME: 10:00
LOCAL: Auditorio I - DIMAp
TITLE:

MalariaApp: A Low-Cost System for Diagnosing Malaria on Thin Blood Smears using Mobile Devices


KEY WORDS:

Malaria, segmentation, classification, parasitemia, arduino, 3D printing


PAGES: 75
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Processamento Gráfico (Graphics)
SUMMARY:

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 train 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.


BANKING MEMBERS:
Presidente - 1350250 - ANNE MAGALY DE PAULA CANUTO
Interno - 2177445 - BRUNO MOTTA DE CARVALHO
Externo ao Programa - 2213126 - VALTER FERREIRA DE ANDRADE NETO
Externo à Instituição - JONES OLIVEIRA DE ALBUQUERQUE - UFRPE
Externo à Instituição - DANIEL LÓPEZ CODINA
Notícia cadastrada em: 20/11/2019 10:09
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