Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/15851
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dc.contributor.authorAnoop B.N.
dc.contributor.authorPavan R.
dc.contributor.authorGirish G.N.
dc.contributor.authorKothari A.R.
dc.contributor.authorRajan J.
dc.date.accessioned2021-05-05T10:28:15Z-
dc.date.available2021-05-05T10:28:15Z-
dc.date.issued2020
dc.identifier.citationBiocybernetics and Biomedical Engineering Vol. 40 , 4 , p. 1343 - 1358en_US
dc.identifier.urihttps://doi.org/10.1016/j.bbe.2020.07.010
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/15851-
dc.description.abstractSegmentation of retinal layers is a vital and important step in computerized processing and the study of retinal Optical Coherence Tomography (OCT) images. However, automatic segmentation of retinal layers is challenging due to the presence of noise, widely varying reflectivity of image components, variations in morphology and alignment of layers in the presence of retinal diseases. In this paper, we propose a Fully Convolutional Network (FCN) termed as DelNet based on a deep ensemble learning approach to selectively segment retinal layers from OCT scans. The proposed model is tested on a publicly available DUKE DME dataset. Comparative analysis with other state-of-the-art methods on a benchmark dataset shows that the performance of DelNet is superior to other methods. © 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciencesen_US
dc.titleStack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography imagesen_US
dc.typeArticleen_US
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