Classification of CT images in COVID-19 and Non-COVID-19 using CNN to extract features and multiple classifiers

  • Edelson Carvalho UFPI
  • Edson Carvalho UFPI

Resumo


O COVID-19 é uma doença respiratória que já infectou mais de 12.3 milhões de pessoas em todo o mundo e é responsável por mais de 556.300 mortes. O diagnóstico precoce do COVID-19 é essencial para a cura e controle da doença. A tomografia computadorizada (TC) apresentou resultados eficientes na avaliação de pacientes com suspeita de infecção por COVID-19. A análise da TC requer o esforço de um especialista, o que pode levar a erros de diagnóstico. O uso de sistemas de diagnóstico auxiliado por computador pode minimizar os problemas gerados pela análise de TCs por especialistas. Este artigo apresenta uma metodologia para diagnosticar a COVID-19 usando CNN para extração de características e múltiplos classificadores em imagens de TC. A metodologia apresentou uma acurácia de 99,79%, recall de 99,79%, precisão de 99,80%, F-score de 0,997, AUC de 0,997 e índice kappa de 0,995. Os resultados obtidos mostram que a metodologia proposta pode ser utilizada como um sistema de auxílio ao diagnóstico.

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Publicado
10/09/2020
CARVALHO, Edelson; CARVALHO, Edson. Classification of CT images in COVID-19 and Non-COVID-19 using CNN to extract features and multiple classifiers. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 8. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 260-267. DOI: https://doi.org/10.5753/ercemapi.2020.11493.