dc.contributor | Frederico Gadelha Guimarães | |
dc.contributor | http://lattes.cnpq.br/2472681535872194 | |
dc.contributor | Antônio de Pádua Braga | |
dc.contributor | Pedro Pedrosa Rebouças Filho | |
dc.contributor | João Paulo Papa | |
dc.contributor | Deborah Aparecida Negrão-Corrêa | |
dc.contributor | Antonio Luiz Pinho Ribeiro | |
dc.creator | Bruno Alberto Soares Oliveira | |
dc.date.accessioned | 2022-08-17T16:30:30Z | |
dc.date.accessioned | 2022-10-03T22:41:48Z | |
dc.date.available | 2022-08-17T16:30:30Z | |
dc.date.available | 2022-10-03T22:41:48Z | |
dc.date.created | 2022-08-17T16:30:30Z | |
dc.date.issued | 2022-03-24 | |
dc.identifier | http://hdl.handle.net/1843/44320 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/3808534 | |
dc.description.abstract | A major public health concern is caused by human intestinal parasites, which are found
largely in tropical countries. The diagnosis of these parasitic diseases is made through
physiological symptoms and fecal examination. Often, few professionals are available and
able to perform this type of examination, which is considered slow, difficult, error-prone,
and can cause eye fatigue in the specialist. Artificial intelligence techniques have been
successfully applied to problems of this nature. Therefore, the objective of this work is to
develop a solution based on deep learning and machine learning to find intestinal parasite
eggs of the species S. mansoni, being a system to aid decision-making in the diagnosis of
fecal examination whose slides were prepared using the Kato-Katz parasitological technique.
A real database was built with 1100 images that were annotated by three different human
specialists in the diagnosis of schistosomiasis. Data augmentation techniques online and
offline were used to obtain a larger number of samples and improve the generalizability
of the tool. As a result, the proposed solution achieved an AP value of 0.884 for an
@[IoU=0.50]. The results and employability of the system are promising, and it could be
used in the SUS to assist health professionals in diagnosing schistosomiasis. | |
dc.publisher | Universidade Federal de Minas Gerais | |
dc.publisher | Brasil | |
dc.publisher | ENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA | |
dc.publisher | Programa de Pós-Graduação em Engenharia Elétrica | |
dc.publisher | UFMG | |
dc.rights | Acesso Aberto | |
dc.subject | Aprendizado de máquina | |
dc.subject | Aprendizado profundo | |
dc.subject | Diagnóstico | |
dc.subject | Esquistossomose | |
dc.subject | Imagens médicas | |
dc.subject | Kato-Katz | |
dc.title | Sistema de diagnóstico da esquistossomose a partir de imagens microscópicas preparadas com a técnica Kato-Katz | |
dc.type | Tese | |