Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images (CROSBI ID 282735)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Božić-Štulić, Dunja ; Braović, Maja ; Stipaničev, Darko
engleski
Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images
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 novel 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%.
optic disc, optic cup, glaucoma, deep learning.
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Podaci o izdanju
Vol 11 (No 2)
2020.
111-120
objavljeno
1847-6996
1847-7003
10.32985/ijeces.11.2.6
Povezanost rada
Računarstvo