O uso de métodos de aprendizado de máquina para classificação de imagens de tomografia computadorizada na pandemia da COVID-19: uma revisão
DOI:
https://doi.org/10.17058/reci.v15i1.19227Palavras-chave:
COVID-19, tomografia computadorizada, aprendizado de máquina, aprendizado profundo, redes neurais convolucionaisResumo
Justificativa e Objetivos: A COVID-19 foi declarada uma pandemia pela Organização Mundial da Saúde, representando um grande desafio em todo o mundo. Um método de diagnóstico precoce da COVID-19 é baseado em tomografias computadorizadas, que podem ser analisadas usando inteligência artificial para economizar recursos médicos, logísticos e humanos. Portanto, o objetivo deste estudo foi apresentar o atual estado da arte na aplicação do aprendizado de máquina para classificar imagens de tomografia computadorizada na pandemia de COVID-19. Conteúdo: A revisão descreve brevemente os tipos de métodos de aprendizado de máquina para detecção de COVID-19, os estágios de construção do modelo de aprendizagem profunda (segmentação, aumento) e aspectos selecionados da inteligência artificial explicável. Finalmente, os resultados da aplicação são discutidos e os indicadores de desempenho mais comuns para modelos individuais são dados. Conclusão: Modelos e algoritmos desenvolvidos durante o pico da pandemia de COVID-19 podem ser reusados no caso de futuros surtos desta ou doenças infecciosas semelhantes.
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