Original scientific paper

https://doi.org/10.32985/ijeces.11.2.6

Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images

Dunja Božić-Štulić ; University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
Maja Braović ; University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
Darko Stipaničev ; University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture

Abstract

Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and DIARETDB1 image datasets and evaluated on MESSIDOR dataset, where it achieved the average accuracy of 97.3% of optic disc and 88.1% of optic cup. Detection rate of glaucoma diesis is 96.75%

Keywords

optic disc, optic cup, glaucoma, deep learning

Hrčak ID:

242975

URI

https://hrcak.srce.hr/242975

Publication date:

19.6.2020.

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