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dc.contributor.authorJha, Debesh
dc.contributor.authorAli, Sharib
dc.contributor.authorTomar, Nikhil Kumar
dc.contributor.authorJohansen, Håvard D.
dc.contributor.authorJohansen, Dag
dc.contributor.authorRittscher, Jens
dc.contributor.authorRiegler, Michael A.
dc.contributor.authorHalvorsen, Pal
dc.date.accessioned2021-12-01T13:42:31Z
dc.date.available2021-12-01T13:42:31Z
dc.date.issued2021-03-04
dc.description.abstractComputer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localization, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localization task. Likewise, the proposed ColonSegNet achieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.en_US
dc.identifier.citationJha, Ali, Tomar, Johansen, Johansen, Rittscher, Riegler, Halvorsen. Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning. IEEE Access. 2021;9:40496-40510en_US
dc.identifier.cristinIDFRIDAID 1926883
dc.identifier.doi10.1109/ACCESS.2021.3063716
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10037/23242
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofJha, D. (2022). Machine Learning-based Classification, Detection, and Segmentation of Medical Images. (Doctoral thesis). <a href=https://hdl.handle.net/10037/23693>https://hdl.handle.net/10037/23693</a>.
dc.relation.journalIEEE Access
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/IKEPLUSS-IKT/263248/Norway/Protecting Shared Data with Privacy Automatons//en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/FORINFRA/270053/Norway/Experimental Infrastructure for Exploration of Exascale Computing//en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.titleReal-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learningen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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