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 featur...
| Permalink: | http://skupni.nsk.hr/Record/nsk.NSK01001088560/Details |
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| Matična publikacija: |
International journal of electrical and computer engineering systems (Online) 11 (2020), 2 ; str. 111-120 |
| Glavni autori: | Božić-Štulić, Dunja (Author), Braović, Maja, Stipaničev, Darko |
| Vrsta građe: | e-članak |
| Jezik: | eng |
| Predmet: | |
| Online pristup: |
https://doi.org/10.32985/ijeces.11.2.6 International journal of electrical and computer engineering systems (Online) Hrčak |
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| 024 | 7 | |2 doi |a 10.32985/ijeces.11.2.6 | |
| 035 | |a (HR-ZaNSK)001088560 | ||
| 040 | |a HR-ZaNSK |b hrv |c HR-ZaNSK |e ppiak | ||
| 041 | 0 | |a eng |b eng | |
| 042 | |a croatica | ||
| 044 | |a ci |c hr | ||
| 080 | 1 | |a 621.3 |2 2011 | |
| 080 | 1 | |a 614 |2 2011 | |
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| 100 | 1 | |a Božić-Štulić, Dunja |4 aut | |
| 245 | 1 | 0 | |a Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images |h [Elektronička građa] / |c Dunja Božić-Štulić, Maja Braović, Darko Stipaničev. |
| 300 | |b Ilustr. | ||
| 504 | |a Bibliografija: 47 jed. | ||
| 504 | |a Abstract. | ||
| 520 | |a 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% | ||
| 653 | 0 | |a Semantička segmentacija |a Segmentacija slike |a Duboko učenje |a Vidni živac |a Medicinska dijagnostika | |
| 700 | 1 | |a Braović, Maja |4 aut | |
| 700 | 1 | |a Stipaničev, Darko |4 aut | |
| 773 | 0 | |t International journal of electrical and computer engineering systems (Online) |x 1847-7003 |g 11 (2020), 2 ; str. 111-120 |w nsk.(HR-ZaNSK)000739692 | |
| 981 | |b Be2020 |b B02/20 | ||
| 998 | |b tino2102 | ||
| 856 | 4 | 0 | |u https://doi.org/10.32985/ijeces.11.2.6 |
| 856 | 4 | 0 | |u http://www.etfos.unios.hr/ijeces/papers/deep-learning-based-approach-for-optic-disc-and-optic-cup-semantic-segmentation-for-glaucoma-analysis-in-retinal-fundus-images/ |y International journal of electrical and computer engineering systems (Online) |
| 856 | 4 | 0 | |u https://hrcak.srce.hr/242975 |y Hrčak |
| 856 | 4 | 1 | |y Digitalna.nsk.hr |