RT info:eu-repo/semantics/article
T1 Comparison of the performance of convolutional neural networks and vision transformer-based systems for automated glaucoma detection with eye fundus images.
A1 Sigut Saavedra, José Francisco
A1 Alayón Miranda, Silvia
A1 Hernández Vidal, Jorge
A1 Fumero, Francisco J.
A1 Díaz Alemán, Tinguaro
K1 Convolutional neural network
K1 Vision transformer-based system
K1 Glaucoma
K1 Fundus imaging
AB Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversibleblindness 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 faceof 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 areno significant differences between the efficiency of both DL strategies for the medical diagnostic problem addressed.
SN 2076-3417
YR 2023
FD 2023
LK http://riull.ull.es/xmlui/handle/915/35094
UL http://riull.ull.es/xmlui/handle/915/35094
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 08-nov-2024