dc.creator | Durán López, Lourdes | es |
dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Amaya Rodríguez, Isabel | es |
dc.creator | Luna Perejón, Francisco | es |
dc.creator | Civit Masot, Javier | es |
dc.creator | Vicente Díaz, Saturnino | es |
dc.creator | Linares Barranco, Alejandro | es |
dc.date.accessioned | 2020-02-12T06:55:07Z | |
dc.date.available | 2020-02-12T06:55:07Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Durán López, L., Domínguez Morales, J.P., Amaya Rodríguez, I., Luna Perejón, F., Civit Masot, J., Vicente Díaz, S. y Linares Barranco, A. (2019). Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks. En IJCCI 2019: 11th International Joint Conference on Computational Intelligence (444-448), Vienna, Austria: ScitePress Digital Library. | |
dc.identifier.isbn | 978-989-758-384-1 | es |
dc.identifier.uri | https://hdl.handle.net/11441/92902 | |
dc.description.abstract | Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer,
the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic
detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors
in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional
Convolutional Neural Networks are able to determine the presence of an object and also its position inside
the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in
mammogram images and propose a detection system that contains: (1) a preprocessing step performed on
mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural
Network model, which performs feature extraction over the mammograms in order to locate tumors within
each image and classify them as malignant or benign. The results obtained show that the proposed algorithm
has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians
when detecting tumors from mammogram images. | 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 | IJCCI 2019: 11th International Joint Conference on Computational Intelligence (2019), pp. 444-448. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Breast Cancer | es |
dc.subject | Mammography | es |
dc.subject | Deep learning | es |
dc.subject | Convolutional Neural Networks (CNN) | es |
dc.subject | Faster Regional Convolutional Neural Network | es |
dc.subject | Medical Image Analysis | es |
dc.title | Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks | 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 | http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0008494304440448 | es |
dc.identifier.doi | 10.5220/0008494304440448 | es |
dc.contributor.group | Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación | es |
idus.format.extent | 5 | es |
dc.publication.initialPage | 444 | es |
dc.publication.endPage | 448 | es |
dc.eventtitle | IJCCI 2019: 11th International Joint Conference on Computational Intelligence | es |
dc.eventinstitution | Vienna, Austria | es |
dc.relation.publicationplace | Setúbal, Portugal | es |