Diagnosticando Tuberculose com Redes Neurais Artificiais e Recursos BPPC
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1106Palavras-chave:
Tuberculose, Redes Neurais, Reconhecimento Automatizado de PadrãoResumo
Tuberculose é uma doença grave e contagiosa que mata milhões de pessoas, sendo um problema de saúde pública global. Enquanto isso, o uso da Inteligência Artificial na radiologia tem despertado crescente interesse de pesquisadores e da indústria. Soluções para auxiliar no diagnóstico já são uma realidade, mas ainda distante de populações vulneráveis e regiões subdesenvolvidas. Logo, soluções acessíveis são essenciais para populações altamente dependentes de ações e serviços públicos. Assim, propomos um método de baixo custo computacional e alta eficiência para auxiliar no diagnóstico de tuberculose. Utilizamos imagens de radiografia torácicas e construímos um modelo de rede neural artificial com recursos BPPC com e sem a geração de dados sintéticos. Os resultados equivalentes à literatura relacionada mostram o desempenho excepcional e de baixo custo da solução, colocando-a como uma solução alternativa viável.
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Copyright (c) 2023 Afonso Ueslei Fonseca, Juliana Paula Felix, Gabriel Silva Vieira, Bruno Moraes Rocha, Emília Alves Nogueira, Carlos Eduardo Egito Araújo, Deborah Fernandes, Fabrizzio Soares
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