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Deep Learning-Based Approach for the Semantic Segmentation of Bright Retinal Damage

  • Cristiana Silva
  • Adrián ColomerEmail author
  • Valery Naranjo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11314)

Abstract

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 U-Net 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-the-art methods.

Keywords

Semantic segmentation Deep learning Fundus images Exudates U-Net 

Notes

Acknowledgements

This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT-2016-2017, 732613]. The work of Adrián Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Cristiana Silva
    • 1
  • Adrián Colomer
    • 2
    Email author
  • Valery Naranjo
    • 2
  1. 1.Campus GualtarUniversity of MinhoBragaPortugal
  2. 2.Instituto de Investigación e Innovación en Bioingeniería (I3B)Universitat Politècnica de ValènciaValenciaSpain

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