KvasirCAM: Diagnóstico Explicável de Doenças Gastrointestinais com Atenção Visual e Otimização Bayesiana

• Marcos R. A. Amorim UFMA
• Neilson P. Ribeiro UFMA / IFMA
• Luana B. da Cruz UFCA
• João O. B. Diniz UFMA / IFMA
• Geraldo B. Júnior UFMA
• João Dallyson S. Almeida UFMA

DOI: https://doi.org/10.5753/sbcas.2026.21416

Resumo

O câncer gastrointestinal representa uma parcela das doenças oncológicas mundiais, sendo o câncer colorretal a terceira neoplasia mais diagnosticada e o câncer de estômago a quinta. A detecção precoce aumenta a sobrevida, sendo a endoscopia o principal exame para identificação dessas patologias. Trabalhos de detecção utilizando Deep Learning têm auxiliado na identificação precoce, contudo a busca de hiperparâmetros não é uma tarefa trivial. Este trabalho propõe um método automático de detecção de patologias gastrointestinais integrando extração de região de interesse, atenção visual, otimização de hiperparâmetros e explicabilidade em arquiteturas CNN. Os resultados demonstram melhora nas arquiteturas, com a ResNet50 alcançando F1-Score de 94,33% e AUC de 98,22%, evidenciando que a abordagem é eficaz no diagnóstico gastrointestinal.

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