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.

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

Pace T.
;
Giglio R.;Tognetto D.;Accardo A.
2022

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.
9781643682846
9781643682853
https://ebooks.iospress.nl/doi/10.3233/SHTI220525
File in questo prodotto:
File Dimensione Formato  
SHTI-294-SHTI220525.pdf

accesso aperto

Descrizione: Articolo
Tipologia: Documento in Versione Editoriale
Licenza: Creative commons
Dimensione 295.1 kB
Formato Adobe PDF
295.1 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3030680
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact