Aplicação de Modelos de Aprendizado Profundo para a Segmentação Semântica de Imagens de Colonoscopia

• Rubens M. G. Aguiar CEIA
• Michel H. Scheeren UTFPR
• Sandro L. de Araujo Junior UTFPR
• Eduardo Mendes UTFPR
• Pedro L. de Paula Filho UTFPR
• Ricardo A. P. Franco UFG

DOI: https://doi.org/10.5753/sbcas.2024.2257

Resumo

Grande parte dos casos de câncer colorretal tem origem em pólipos colorretais. A elevação na taxa de detecção de pólipos reduz o risco de desenvolvimento dessa patologia. Por isso, a implementação de redes neurais para detectar e segmentar tais pólipos representa uma estratégia que gera resultados impactantes. Por isso, surge a necessidade de comparar os diferentes modelos disponíveis na literatura e identificar as melhores opções para segmentação de polípos. De fato, a partir de do conjunto de dados Kvasir, foram selecionados modelos com potencial e foram executado os treinamentos e avaliações de seus desempenhos. Efetivamente, foram treinados 8 modelos, nos quais foram identificadas as arquiteturas mais eficientes para a segmentação de pólipos no conjunto de dados, por exemplo, o modelo ESFPNet alcançou o maior DICE (0,9008).

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