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Descripción
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| Glaucoma is the leading cause of global irreversible vision loss with prevalence for population aged 40-80 estimated in 3-4%. Screening is a useful strategy as early diagnosis and treatment of the condition can prevent vision loss. Several studies have proposed successfully the classification of glaucoma from color fundus images using Convolutional Neural Networks (CNNs). We propose the application of a combination of state-of-the-art CNNs with transfer learning to classify glaucoma using color fundus images. | |
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Internacional
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Si |
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Nombre congreso
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IEEE International Symposium on Biomedical Imaging.(ISBI) |
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Tipo de participación
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970 |
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Lugar del congreso
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Venecia (Italia) |
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Revisores
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Si |
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ISBN o ISSN
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DOI
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Fecha inicio congreso
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08/04/2019 |
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Fecha fin congreso
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11/04/2019 |
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Título de las actas
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