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Deep Learning-Based Object Detection Algorithms in Medical Imaging

2025-01-15Recensão Acesso aberto
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dc.contributor.authorAlbuquerque, Carina
dc.contributor.authorHenriques, Roberto
dc.contributor.authorCastelli, Mauro
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblElsevier
dc.date.accessioned2024-12-18T17:41:44Z
dc.date.available2024-12-18T17:41:44Z
dc.date.issued2025-01-15
dc.descriptionAlbuquerque, C., Henriques, R., & Castelli, M. (2025). Deep Learning-Based Object Detection Algorithms in Medical Imaging: Systematic Review. Heliyon, 11(1), 1-23. Article e41137. https://doi.org/10.1016/j.heliyon.2024.e41137 ---This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
dc.description.abstractOver the past decade, Deep Learning (DL) techniques have demonstrated remarkable advancements across various domains, driving their widespread adoption. Particularly in medical image analysis, DL received greater attention for tasks like image segmentation, object detection, and classification. This paper provides an overview of DL-based object recognition in medical images, exploring recent methods and emphasizing different imaging techniques and anatomical applications. Utilizing a meticulous quantitative and qualitative analysis following PRISMA guidelines, we examined publications based on citation rates to explore into the utilization of DL-based object detectors across imaging modalities and anatomical domains. Our findings reveal a consistent rise in the utilization of DL-based object detection models, indicating unexploited potential in medical image analysis. Predominantly within Medicine and Computer Science domains, research in this area is most active in the US, China, and Japan. Notably, DL-based object detection methods have gotten significant interest across diverse medical imaging modalities and anatomical domains. These methods have been applied to a range of techniques including CR scans, pathology images, and endoscopic imaging, showcasing their adaptability. Moreover, diverse anatomical applications, particularly in digital pathology and microscopy, have been explored. The analysis underscores the presence of varied datasets, often with significant discrepancies in size, with a notable percentage being labeled as private or internal, and with prospective studies in this field remaining scarce. Our review of existing trends in DL-based object detection in medical images offers insights for future research directions. The continuous evolution of DL algorithms highlighted in the literature underscores the dynamic nature of this field, emphasizing the need for ongoing research and fitted optimization for specific applications.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent23
dc.format.extent6127382
dc.identifier.doi10.1016/j.heliyon.2024.e41137
dc.identifier.issn2405-8440
dc.identifier.otherPURE: 104701975
dc.identifier.otherPURE UUID: 48247a5f-449c-4435-b5ae-aba480b44834
dc.identifier.otherScopus: 85211618807
dc.identifier.otherWOS: 001770609700001
dc.identifier.otherPubMed: 39758372
dc.identifier.otherPubMedCentral: PMC11699422
dc.identifier.urihttp://hdl.handle.net/10362/176499
dc.identifier.urlhttps://www.scopus.com/pages/publications/85211618807
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001770609700001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectdeep learning
dc.subjectobject detection
dc.subjectMedical imaging
dc.subjectbibliometric analysis
dc.subjectqualitative analysis
dc.subjectquantitative analysis
dc.subjectGeneral
dc.subjectSDG 3 - Good Health and Well-being
dc.titleDeep Learning-Based Object Detection Algorithms in Medical Imagingen
dc.title.subtitleSystematic Reviewen
dc.typereview
degois.publication.firstPage1
degois.publication.issue1
degois.publication.lastPage23
degois.publication.titleHeliyon
degois.publication.volume11
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)