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A Deep Learning Method for Automatic Identification of Drusen and Macular Hole from Optical Coherence Tomography

Pace T.
•
Degan N.
•
Giglio R.
altro
Accardo A.
2022
  • conference object

Abstract
Deep Learning methods have become dominant in various fields of medical imaging, including ophthalmology. In this preliminary study, we investigated a method based on Convolutional Neural Network for the identification of drusen and macular hole from Optical Coherence Tomography scans with the aim to assist ophthalmologists in diagnosing and assessing retinal diseases.
DOI
10.3233/SHTI220525
Archivio
https://hdl.handle.net/11368/3030680
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85131105823
https://ebooks.iospress.nl/doi/10.3233/SHTI220525
Diritti
open access
license:creative commons
license uri:http://creativecommons.org/licenses/by-nc/4.0/
FVG url
https://arts.units.it/bitstream/11368/3030680/1/SHTI-294-SHTI220525.pdf
Soggetti
  • Convolutional Neural ...

  • Deep Learning

  • Drusen

  • Macular Hole

  • Optical Coherence Tom...

  • Retinal disease

  • Human

  • Retina

  • Tomography, Optical C...

  • Deep Learning

  • Retinal Disease

  • Retinal Perforations

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