Meta Aprendizagem Aplicada ao Diagnóstico de Glaucoma / Learning Goal Applied to Glaucoma Diagnosis

Arthur Guilherme Santos Fernandes, Caio Manfredini da Silva Martins, Geraldo Braz Junior, José Mateus Carvalho Boaro, Lisle Faray de Paiva

Abstract


O glaucoma é uma doença silenciosa que pode levar a cegueira caso não seja tratada com urgência. Métodos de diagnóstico que utilizam inteligên- cia computacional têm sido propostos com a finalidade de aumentar a taxa de detecções da doença ainda na sua fase inicial, e proporcionar melhor qualidade de vida aos pacientes. Porém, a descoberta de melhores técnicas e métodos de diagnóstico automatizado, é necessária grande quantidade de testes de diferen- tes metodologias e abordagens sobre o problema, tornando o processo lento e sujeito a erros. Este trabalho propõe uma solução através da meta aprendiza- gem de métodos de pré processamento, decomposição, extração de caracterís- ticas que devem ser usados de maneira eficiente para solucionar o problema. Os resultados obtidos são promissores, atingindo 93,40% de acurácia após 144 execuções e deve melhorar proporcionalmente à quantidade de testes realiza- dos.


Keywords


Diagnóstico Assistido por Computadores, Meta Aprendiza- gem, Otimização Bayesiana, Diagnóstico de Glaucoma, Extração de Carac- terísticas.

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DOI: https://doi.org/10.34117/bjdv6n7-010

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