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Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data

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dc.contributor.authorSánchez Cauce, Raquel
dc.contributor.authorPérez Martín, Jorge
dc.contributor.authorLuque Gallego, Manuel
dc.contributor.funderAgencia Estatal de Investigación (España)
dc.date.accessioned2026-01-30T08:54:29Z
dc.date.available2026-01-30T08:54:29Z
dc.date.issued2021-06
dc.descriptionThis is the accepted manuscript of the article. The registered version was first published in Computer Methods and Programs in Biomedicine, 204, is available online at the publisher's website: https://doi.org/10.1016/j.cmpb.2021.106045
dc.descriptionEste es el manuscrito aceptado del artículo. La versión registrada fue publicada por primera vez en Computer Methods and Programs in Biomedicine, 204, está disponible en línea en el sitio web del editor: Computer Methods and Programs in Biomedicine, 204
dc.description.abstractBackground and objective Breast cancer is the most common cancer in women. While mammography is the most widely used screening technique for the early detection of this disease, it has several disadvantages such as radiation exposure or high economic cost. Recently, multiple authors studied the ability of machine learning algorithms for early diagnosis of breast cancer using thermal images, showing that thermography can be considered as a complementary test to mammography, or even as a primary test under certain circumstances. Moreover, although some personal and clinical data are considered risk factors of breast cancer, none of these works considered that information jointly with thermal images. Methods We propose a novel approach for early detection of breast cancer combining thermal images of different views with personal and clinical data, building a multi-input classification model which exploits the benefits of convolutional neural networks for image analysis. First, we searched for structures using only thermal images. Next, we added the clinical data as a new branch of each of these structures, aiming to improve its performance. Results We applied our method to the most widely used public database of breast thermal images, the Database for Mastology Research with Infrared Image. The best model achieves a 97% accuracy and an area under the ROC curve of 0.99, with a specificity of 100% and a sensitivity of 83%. Conclusions After studying the impact of thermal images and personal and clinical data on multi-input convolutional neural networks for breast cancer diagnosis, we conclude that: (1) adding the lateral views to the front view improves the performance of the classification model, and (2) including personal and clinical data helps the model to recognize sick patients.en
dc.description.provenanceMade available in DSpace on 2026-01-30T08:54:29Z (GMT). No. of bitstreams: 1 sanchezcauce2021-for-public-repository_MANUEL LUQUE GALLEGO.pdf: 536879 bytes, checksum: 10556e0fba36f6c71639b47d79d9fd63 (MD5) Previous issue date: 2021-06en
dc.description.sponsorshipThis work was partially supported by the grant TIN2016-77206-R from the Spanish Government, co-financed by the European Regional Development Fund. Also, R. Sánchez-Cauce received a postdoctoral grant (PEJD-2018-POST/TIC-9490) from Universidad Nacional de Educación a Distancia (UNED), co-financed by the Regional Government of Madrid with funds from the Youth Employment Initiative (YEI) of the European Social Fund.en
dc.description.versionversión final
dc.identifier.citationSánchez-Cauce, R., Pérez-Martín, J., & Luque, M. (2021). Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data. Computer Methods and Programs in Biomedicine, 204. https://doi.org/10.1016/J.CMPB.2021.106045
dc.identifier.doihttps://doi.org/10.1016/j.cmpb.2021.106045
dc.identifier.eissn1872-7565
dc.identifier.issn0169-2607
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31647
dc.journal.titleComputer Methods and Programs in Biomedicine
dc.journal.volume204
dc.language.isoen
dc.publisherElsevier
dc.relation.centerFacultad de Ciencias
dc.relation.departmentEstadística, Investigación Operativa y Cálculo Numérico
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-77206-R/ES/ANALISIS DE COSTE-EFECTIVIDAD MEDIANTE REDES DE ANALISIS DE DECISIONES
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject3201.01 Oncología
dc.subject3201.11 Radiología
dc.subject.keywordsBreast canceren
dc.subject.keywordsClassificationen
dc.subject.keywordsClinical dataen
dc.subject.keywordsConvolutional neural networken
dc.subject.keywordsThermal imagesen
dc.subject.odsODS 3 - Salud y bienestar
dc.subject.odsODS 9 - Industria, innovación e infraestructura
dc.titleMulti-input convolutional neural network for breast cancer detection using thermal images and clinical dataen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublicatione94799f0-bdd0-45be-b6b2-426269f6ee46
relation.isAuthorOfPublication.latestForDiscoveryfda2a608-1b8f-46a1-87cc-777559fcf158
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