Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network

Intriago Pazmiño, Monserrate and Ibarra Fiallo, Julio and Alonso Calvo, Raúl and Crespo Del Arco, José (2019). Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network. In: "Second International Conference on Data Science, E-Learning and Information Systems (DATA '19)", 02-05 Dic 2019, Dubai, Emiratos Árabes. ISBN 978-1-4503-7284-8. pp. 1-5. https://doi.org/10.1145/3368691.3368711.

Description

Title: Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network
Author/s:
  • Intriago Pazmiño, Monserrate
  • Ibarra Fiallo, Julio
  • Alonso Calvo, Raúl
  • Crespo Del Arco, José
Item Type: Presentation at Congress or Conference (Article)
Event Title: Second International Conference on Data Science, E-Learning and Information Systems (DATA '19)
Event Dates: 02-05 Dic 2019
Event Location: Dubai, Emiratos Árabes
Title of Book: DATA '19: Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems
Date: 2019
ISBN: 978-1-4503-7284-8
Subjects:
Freetext Keywords: Retinopathy of prematurity; Convolutional neural network; Medical image processing
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 65283
DC Identifier: http://oa.upm.es/65283/
OAI Identifier: oai:oa.upm.es:65283
DOI: 10.1145/3368691.3368711
Official URL: https://dl.acm.org/doi/pdf/10.1145/3368691.3368711
Deposited by: Memoria Investigacion
Deposited on: 10 Nov 2020 07:25
Last Modified: 10 Nov 2020 07:25
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