Segmentation of optic disc and cup in retinal images using of deep learning approaches

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148718" target="_blank" >RIV/00216305:26220/23:PU148718 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_1.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_1.pdf</a>
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Segmentation of optic disc and cup in retinal images using of deep learning approaches
Original language description
This paper presents a comparative analysis of optic disc and cup segmentation in retinal fundus images using two deep learning models: the classical U-net and its modified version, nnU-Net. The models were trained and tested on publicly available databases consisting of 1295 images for training and 555 images for testing. The results indicate that while nnU-Net demonstrated only slight improvements in disc segmentation on the test database, it significantly outperformed the U-net model in optical cup segmentation.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA21-18578S" target="_blank" >GA21-18578S: Dual-wavelength functional retinal imaging and simultaneous biosignals acquisition for ocular blood circulation assessment</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)