Show simple item record

dc.contributor.authorSigut Saavedra, José Francisco 
dc.contributor.authorAlayón Miranda, Silvia
dc.contributor.authorHernández Vidal, Jorge
dc.contributor.authorFumero, Francisco J.
dc.contributor.authorDíaz Alemán, Tinguaro
dc.date.accessioned2023-12-27T21:05:51Z
dc.date.available2023-12-27T21:05:51Z
dc.date.issued2023
dc.identifier.issn2076-3417
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/35094
dc.description.abstractGlaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification of eye fundus images. Among these intelligent systems, Convolutional Neural Networks (CNNs) stand out, although alternatives have recently appeared, such as Vision Transformers (ViTs) or hybrid systems, which are also highly efficient in image processing. The question that arises in the face of so many emerging methods is whether all these new techniques are really more efficient for the problem of glaucoma diagnosis than the CNNs that have been used so far. In this article, we present a comprehensive comparative study of all these DL models in glaucoma detection, with the aim of elucidating which strategies are significantly better. Our main conclusion is that there are no significant differences between the efficiency of both DL strategies for the medical diagnostic problem addressed.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesApplied Sciences. 2023, vol. 13, núm. 12722.
dc.rightsLicencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
dc.titleComparison of the performance of convolutional neural networks and vision transformer-based systems for automated glaucoma detection with eye fundus images.
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.3390/app132312722
dc.subject.keywordConvolutional neural networken
dc.subject.keywordVision transformer-based systemen
dc.subject.keywordGlaucomaen
dc.subject.keywordFundus imagingen


Files in this item

This item appears in the following Collection(s)

Show simple item record

Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
Except where otherwise noted, this item's license is described as Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)