Descripción
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In this paper, we describe the experimentation with a convolutional neural network for segmenting retinal net from pathological fundus images of preterm born children. Segmenting retinal net from pathological fundus images is a fundamental task to aid computer diagnosis. We used U-net architecture for training and testing. Testing with ROPFI dataset, we obtained an area under the receiver operating curve equal to 0.9180; when average sensitivity is equal to 0.700, the average specificity is equal to 0.9710. This performance is higher than prior works using a similar dataset. | |
Internacional
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Si |
Nombre congreso
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DATA '19: Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems |
Tipo de participación
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960 |
Lugar del congreso
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Revisores
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Si |
ISBN o ISSN
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978-1-4503-7284-8 |
DOI
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https://doi.org/10.1145/3368691.3368711 |
Fecha inicio congreso
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09/12/2019 |
Fecha fin congreso
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12/12/2019 |
Desde la página
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1 |
Hasta la página
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5 |
Título de las actas
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DATA '19: Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems |