dc.creator | Amaya Rodríguez, Isabel | es |
dc.creator | Durán López, Lourdes | es |
dc.creator | Luna Perejón, Francisco | es |
dc.creator | Civit Masot, Javier | es |
dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Vicente Díaz, Saturnino | es |
dc.creator | Civit Balcells, Antón | es |
dc.creator | Cascado Caballero, Daniel | es |
dc.creator | Linares Barranco, Alejandro | es |
dc.date.accessioned | 2019-12-18T09:07:09Z | |
dc.date.available | 2019-12-18T09:07:09Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Amaya Rodríguez, I., Durán López, L., Luna Perejón, F., Civit Masot, J., Domínguez Morales, J.P., Vicente Díaz, S.,...,Linares Barranco, A. (2019). Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI. En UCCI 2019: 11th International Joint Conference on Computational Intelligence (528-535), Vienna Austria: ScitePress Digital Library. | |
dc.identifier.isbn | 978-989-758-384-1 | es |
dc.identifier.uri | https://hdl.handle.net/11441/91058 | |
dc.description.abstract | Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor.
Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery.
Deep-learning techniques are becoming popular in medical applications and image-based diagnosis.
Convolutional Neural Networks are the preferred architecture for object detection and classification in images.
In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma
detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing
the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using
Fuzzy C-Means. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TEC2016-77785-P | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | ScitePress Digital Library | es |
dc.relation.ispartof | UCCI 2019: 11th International Joint Conference on Computational Intelligence (2019), pp. 528-535. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep learning | es |
dc.subject | Convolutional Neural Networks (CNN) | es |
dc.subject | LeNet | es |
dc.subject | GoogleNet | es |
dc.subject | Fuzzy C-Means (FCM) | es |
dc.subject | Glioma | es |
dc.subject | Diagnosis Aids | es |
dc.subject | Magnetic Resonance Imaging (MRI) | es |
dc.title | Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI | es |
dc.type | info:eu-repo/semantics/conferenceObject | 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.projectID | TEC2016-77785-P | es |
dc.relation.publisherversion | https://www.scitepress.org/PublicationsDetail.aspx?ID=VgcDVIojzhE=&t=1 | es |
dc.identifier.doi | 10.5220/0008494005280535 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación | es |
idus.format.extent | 8 | es |
dc.publication.initialPage | 528 | es |
dc.publication.endPage | 535 | es |
dc.eventtitle | UCCI 2019: 11th International Joint Conference on Computational Intelligence | es |
dc.eventinstitution | Vienna Austria | es |
dc.relation.publicationplace | Setúbal, Portugal | es |