Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage
Regular screening for the development of diabetic retinopathy is imperative for an early diagnosis and a timely treatment, thus preventing further progression of the disease. The conventional screening techniques based on manual observation by qualified physicians can be very time consuming and pron...
| Autores: | , , |
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| Formato: | capítulo de livro |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/124075 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/124075 |
| Access Level: | acceso abierto |
| Palavra-chave: | Semantic segmentation Deep learning Fundus images Exudates U-Net TEORIA DE LA SEÑAL Y COMUNICACIONES |
| Resumo: | Regular screening for the development of diabetic retinopathy is imperative for an early diagnosis and a timely treatment, thus preventing further progression of the disease. The conventional screening techniques based on manual observation by qualified physicians can be very time consuming and prone to error. In this paper, a novel automated screening model based on deep learning for the semantic segmentation of exudates in color fundus images is proposed with the implementation of an end-to-end convolutional neural network built upon UNet architecture. This encoder-decoder network is characterized by the combination of a contracting path and a symmetrical expansive path to obtain precise localization with the use of context information. The proposed method was validated on E-OPHTHA and DIARETDB1 public databases achieving promising results compared to current state-of-theart methods. |
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