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dc.contributor.authorTahmooresi, Maryam
dc.contributor.authorRemondo Bueno, David
dc.contributor.authorAlcober Segura, Jesús Ángel
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Telemàtica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica
dc.date.accessioned2021-10-19T11:54:44Z
dc.date.issued2021
dc.identifier.citationTahmooresi, M.; Remondo, D.; Alcober, J. Breast cancer detection using machine learning with thermograms in an edge computing scenario. A: International Workshop on Deep Learning for Wellbeing Applications Leveraging Mobile Devices and Edge Computing. "HealthDL'21: 2nd Workshop on Deep Learning for Wellbeing Applications Leveraging Mobile Devices and Edge Computing: Virtual Conference, WI, USA: June 24, 2021: proceedings". New York: Association for Computing Machinery (ACM), 2021, p. 13-16. ISBN 978-1-4503-8598-5. DOI 10.1145/3469258.3469850.
dc.identifier.isbn978-1-4503-8598-5
dc.identifier.urihttp://hdl.handle.net/2117/353841
dc.description.abstractThe second cause of death in the world is cancer. Although breast cancer is the more common cause of death among women, the chance of survival can be increased by detecting cancer in the early stages. For this aim, there are different tests such as Magnetic Resonance Imaging (MRI), mammogram, ultrasound, thermogram and among these tests, the mammogram is the one which is used more frequently. Regarding the advantages and the results, which are achieved by thermogram, it can be a good alternative or complement for the mammogram if we can improve the weaknesses of the thermogram. For this reason, in this research, we work on a thermogram to see the possibility of having a good performance. On the other hand, we train another model by sending personal patients' information to see the effect of these data to improve the performance of breast cancer detection. In the end, we plan to separate the process between edge and core host to do the process faster, safer, and cost-effective.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherAssociation for Computing Machinery (ACM)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament del senyal en les telecomunicacions
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Aspectes socials
dc.subject.lcshBroadband communication systems
dc.subject.lcshComputational neuroscience
dc.subject.otherComputing methodologies
dc.subject.otherMachine learning
dc.subject.otherMachine learning approaches
dc.subject.otherNeural networks
dc.titleBreast cancer detection using machine learning with thermograms in an edge computing scenario
dc.typeConference lecture
dc.subject.lemacTelecomunicació de banda ampla, Sistemes de
dc.subject.lemacNeurociència computacional
dc.contributor.groupUniversitat Politècnica de Catalunya. BAMPLA - Disseny i Avaluació de Xarxes i Serveis de Banda Ampla
dc.identifier.doi10.1145/3469258.3469850
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://dl.acm.org/doi/10.1145/3469258.3469850
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac31887722
dc.description.versionPostprint (author's final draft)
dc.date.lift10000-01-01
local.citation.authorTahmooresi, M.; Remondo, D.; Alcober, J.
local.citation.contributorInternational Workshop on Deep Learning for Wellbeing Applications Leveraging Mobile Devices and Edge Computing
local.citation.pubplaceNew York
local.citation.publicationNameHealthDL'21: 2nd Workshop on Deep Learning for Wellbeing Applications Leveraging Mobile Devices and Edge Computing: Virtual Conference, WI, USA: June 24, 2021: proceedings
local.citation.startingPage13
local.citation.endingPage16


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