Name
Title | ||
|---|---|---|
Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network |
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.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3368691.3368711 | Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems |
Keywords | Field | DocType |
convolutional neural network, medical image processing, retinopathy of prematurity | Retinopathy of prematurity,Receiver operating characteristic,Pattern recognition,Convolutional neural network,Computer science,Fundus (eye),Artificial intelligence,Retinal | Conference |
ISBN | Citations | PageRank |
978-1-4503-7284-8 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Monserrate Intriago-Pazmiño | 1 | 0 | 0.68 |
| Julio Ibarra-Fiallo | 2 | 3 | 2.46 |
| Raúl Alonso-Calvo | 3 | 0 | 0.34 |
| José Crespo | 4 | 0 | 0.34 |