dc.creator | Civit Masot, Javier | es |
dc.creator | Domínguez Morales, Manuel Jesús | es |
dc.creator | Vicente Díaz, Saturnino | es |
dc.creator | Civit Balcells, Antón | es |
dc.date.accessioned | 2021-02-26T09:09:41Z | |
dc.date.available | 2021-02-26T09:09:41Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Civit Masot, J., Domínguez Morales, M.J., Vicente Díaz, S. y Civit Balcells, A. (2020). Dual Machine-Learning system to aid Glaucoma Diagnosis using disc and cup feature extraction.. IEEE Access, 8, 127519-127529. | |
dc.identifier.issn | 2169-3536 | es |
dc.identifier.uri | https://hdl.handle.net/11441/105491 | |
dc.description.abstract | Glaucoma is a degenerative disease that affects vision, causing damage to the optic nerve
that ends in vision loss. The classic techniques to detect it have undergone a great change since the intrusion
of machine learning techniques into the processing of eye fundus images. Several works focus on training
a convolutional neural network (CNN) by brute force, while others use segmentation and feature extraction
techniques to detect glaucoma. In this work, a diagnostic aid tool to detect glaucoma using eye fundus
images is developed, trained and tested. It consists of two subsystems that are independently trained and
tested, combining their results to improve glaucoma detection. The first subsystem applies machine learning
and segmentation techniques to detect optic disc and cup independently, combine them and extract their
physical and positional features. The second one applies transfer learning techniques to a pre-trained CNN
to detect glaucoma through the analysis of the complete eye fundus images. The results of both systems are
combined to discriminate positive cases of glaucoma and improve final detection. The results show that this
system achieves a higher classification rate than previous works. The system also provides information on
the basis for the proposed diagnosis suggestion that can help the ophthalmologist to accept or modify it. | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Access, 8, 127519-127529. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Glaucoma | es |
dc.subject | Ensemble networks | es |
dc.subject | Medical diagnostic aids | es |
dc.subject | Medical imaging | es |
dc.subject | Explainable AI | es |
dc.title | Dual Machine-Learning system to aid Glaucoma Diagnosis using disc and cup feature extraction. | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9138371 | es |
dc.identifier.doi | 10.1109/ACCESS.2020.3008539 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación | es |
dc.journaltitle | IEEE Access | es |
dc.publication.volumen | 8 | es |
dc.publication.initialPage | 127519 | es |
dc.publication.endPage | 127529 | es |