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Deep learning in multimedia healthcare applications: a review

dc.contributor.authorTobón D.P
dc.contributor.authorV, Hossain M.S
dc.contributor.authorMuhammad G
dc.contributor.authorBilbao J
dc.contributor.authorSaddik A.E.
dc.date.accessioned2023-10-24T19:24:56Z
dc.date.available2023-10-24T19:24:56Z
dc.date.created2022
dc.identifier.issn9424962
dc.identifier.urihttp://hdl.handle.net/11407/8019
dc.description.abstractThe increase in chronic diseases has affected the countries’ health system and economy. With the recent COVID-19 virus, humanity has experienced a great challenge, which has led to make efforts to detect it and prevent its spread. Hence, it is necessary to develop new solutions that are based on technology and low cost, to satisfy the citizens’ needs. Deep learning techniques is a technological solution that has been used in healthcare lately. Nowadays, with the increase in chips processing capabilities, increase size of data, and the progress in deep learning research, healthcare applications have been proposed to provide citizens’ health needs. In addition, a big amount of data is generated every day. Development in Internet of Things, gadgets, and phones has allowed the access to multimedia data. Data such as images, video, audio and text are used as input of applications based on deep learning methods to support healthcare system to diagnose, predict, or treat patients. This review pretends to give an overview of proposed healthcare solutions based on deep learning techniques using multimedia data. We show the use of deep learning in healthcare, explain the different types of multimedia data, show some relevant deep learning multimedia applications in healthcare, and highlight some challenges in this research area. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.eng
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85130972021&doi=10.1007%2fs00530-022-00948-0&partnerID=40&md5=8be843f849f9658afcd679101592d597
dc.sourceMultimedia Syst
dc.sourceMultimedia Systemseng
dc.subjectChronic diseaseeng
dc.subjectCOVID-19eng
dc.subjectDeep learningeng
dc.subjectHealthcareeng
dc.subjectMonomediaeng
dc.subjectMultimediaeng
dc.subjectMultimodaleng
dc.titleDeep learning in multimedia healthcare applications: a revieweng
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.type.spaArtículo
dc.identifier.doi10.1007/s00530-022-00948-0
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationTobón, D.P., V, Department of Telecommunications Engineering, Universidad de Medellín, Medellín, Colombia
dc.affiliationHossain, M.S., Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
dc.affiliationMuhammad, G., Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
dc.affiliationBilbao, J., Head of Research Department - ICT (IoT Digital Platforms, Data Analytics & Artificial Intelligence) IKERLAN, Arrasate, Spain
dc.affiliationSaddik, A.E., Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates, University of Ottawa, Ottawa, Canada
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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