Análise comparativa entre redes neurais convolucionais para classificação de pacientes com Covid-19 / Comparative analysis between convolutional neural networks for classification of patients with Covid-19
DOI:
https://doi.org/10.34119/bjhrv5n2-099Keywords:
cnn, raio-x, covid-19.Abstract
Diante da pandemia do COVID-19, percebemos a necessidade de associar a tecnologia para otimizar a identificação dessa doença. No contexto do elevado número de testes e, além disso, resultados falsos, surgiu um novo método de diagnóstico eficiente para aliviar a propagação desse vírus. A presente pesquisa usa uma ferramenta de teste alternativo com raio-x de tórax dos pacientes como identificador. Assim, esse sistema pode auxiliar o médico a examinar imagens pulmonares, reduzir o tempo para análise e aumentar a precisão do diagnóstico. Este artigo tem como objetivo desenvolver um modelo de aprendizado profundo utilizando Redes Neurais Convolucionais(CNN), chamado CNN-IF, e analisar o seu desempenho com outros modelos já existentes. Além de analisar a presença do diafragma nas imagens utilizadas para treino. Diante dos resultados obtidos, a pesquisa se mostrou promissora para o auxílio no diagnóstico de pacientes com COVID-19, classificando em pacientes saudáveis ou com COVID-19. Com isso foi possível chegar à acurácia de 98,5%, precisão de 96,6% e sensibilidade de 97,7%.
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