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

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 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.This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an 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.Silva, C.; Colomer, A.; Naranjo Ornedo, V. (2018). Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 164-173. https://doi.org/10.1007/978-3-030-03493-1_18S164173World Health Organization: Diabetes fact sheet. Sci. Total Environ. 20, 1–88 (2011)Verma, L., Prakash, G., Tewari, H.K.: Diabetic retinopathy: time for action. No complacency please! Bull. 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