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Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review

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dc.contributor.author Jiménez-Gaona, Yuliana es_ES
dc.contributor.author Rodríguez Álvarez, María José es_ES
dc.contributor.author Lakshminarayanan, Vasudevan es_ES
dc.date.accessioned 2021-03-05T04:32:58Z
dc.date.available 2021-03-05T04:32:58Z
dc.date.issued 2020-11 es_ES
dc.identifier.uri http://hdl.handle.net/10251/163199
dc.description.abstract [EN] This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010-January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies. es_ES
dc.description.sponsorship This project has been co-financed by the Spanish Government Grant PID2019-107790RB-C22, "Software development for a continuous PET crystal systems applied to breast cancer". es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Applied Sciences es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Breast cancer es_ES
dc.subject Computer-aided diagnosis es_ES
dc.subject Convolutional neural networks es_ES
dc.subject Deep learning es_ES
dc.subject Mammography es_ES
dc.subject Ultrasound es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/app10228298 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107790RB-C22/ES/DESARROLLO DEL SOFTWARE PARA UN SISTEMA PET DE CRISTAL CONTINUO APLICADO AL CANCER DE MAMA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular es_ES
dc.description.bibliographicCitation Jiménez-Gaona, Y.; Rodríguez Álvarez, MJ.; Lakshminarayanan, V. (2020). Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review. Applied Sciences. 10(22):1-29. https://doi.org/10.3390/app10228298 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/app10228298 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 29 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 22 es_ES
dc.identifier.eissn 2076-3417 es_ES
dc.relation.pasarela S\422203 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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