Diagnóstico de Glaucoma Utilizando Atributos de Textura e CNN’s Pré-treinadas

Authors

DOI:

https://doi.org/10.22456/2175-2745.76387

Keywords:

medical images, glaucoma diagnosis, texture features, transfer learning

Abstract

Glaucoma é uma doença ocular que danifica o nervo óptico causando a perda da visão. Ela é a segunda principal causa de cegueira no mundo. Vários sistemas de diagnóstico automático de glaucoma têm sido propostos, contudo é possível realizar melhorias nestas técnicas, visto que, os sistemas atuais não lidam com uma grande diversidade de imagens. Assim, este trabalho visa realizar a detecção automática do glaucoma nas imagens da retina, através do uso de descritores de textura e Redes Neurais Convolucionais (CNNs). Os resultados mostraram que a junção dos descritores GLCM e CNNs e a utilização do classificador Random Forest são promissores na detecção dessa patologia, obtendo uma acurácia de 91,06% em 873 imagens de 4 bases de dados públicas.

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Author Biographies

Maíla Lima Claro, Universidade Federal do Piauí

Departamento de Computação

Rodrigo de Melo Souza Veras, Universidade Federal do Piauí

Departamento de Computação

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Published

2018-02-18

How to Cite

Claro, M. L., Veras, R. de M. S., Santana, A. M., Vogado, L. H. S., & Sousa, L. P. (2018). Diagnóstico de Glaucoma Utilizando Atributos de Textura e CNN’s Pré-treinadas. Revista De Informática Teórica E Aplicada, 25(1), 82–89. https://doi.org/10.22456/2175-2745.76387

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Section

Regular Papers

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