Evaluation of Texture Maps as Input to Extract Deep Features in Glaucoma Diagnosis

  • Daniel Silva Universidade Federal do Piauí
  • Romuere Silva Universidade Federal do Piauí

Resumo


Glaucoma is a significant cause of blindness in the world. Doctors use computerized images to detect these diseases. Early detection of the disease increases the chances of treatment, reducing the adverse effects. This work proposes an evaluation of texture maps combinations as input to Convolutional Neural Networks for glaucoma classification in retinal images. In our experiments, we used three textures maps, three CNN architectures, and three classifiers. We achieve a Kappa =0.708±0.054 and a Accuracy = 0.859±0.021. We conclude that using the combination of texture maps can improve the automatic detection of glaucoma compared to single-channel inputs, and could be used by state-of-the-art methods to improve their classification rates.

Palavras-chave: Glaucoma, Convolutional Neural Networks

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Publicado
20/10/2020
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SILVA, Daniel; SILVA, Romuere. Evaluation of Texture Maps as Input to Extract Deep Features in Glaucoma Diagnosis. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 459-470. DOI: https://doi.org/10.5753/eniac.2020.12151.