Deep learning models for colorectal polyps

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dc.contributor.authorBardhi, Ornela
dc.contributor.authorSierra-Sosa, Daniel
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorBujanda Fernández de Piérola, Luis
dc.date.accessioned2025-05-09T05:54:20Z
dc.date.available2025-05-09T05:54:20Z
dc.date.issued2021-06-10
dc.date.updated2025-05-09T05:54:20Z
dc.description.abstractColorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to dateen
dc.description.sponsorshipO.B. received funding from the European Union´s Horizon 2020 CATCH ITN project under the Marie Sklodowska-Curie grant agreement no. 722012en
dc.identifier.citationBardhi, O., Sierra-Sosa, D., Garcia-Zapirain, B., & Bujanda, L. (2021). Deep learning models for colorectal polyps. Information (Switzerland), 12(6). https://doi.org/10.3390/INFO12060245
dc.identifier.doi10.3390/INFO12060245
dc.identifier.eissn2078-2489
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2697
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2021 by the authors
dc.subject.otherColon cancer
dc.subject.otherDeep learning
dc.subject.otherDetection
dc.subject.otherClassification
dc.subject.otherLocalization
dc.subject.otherCNN
dc.subject.otherAutoencoders
dc.titleDeep learning models for colorectal polypsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue6
oaire.citation.titleInformation (Switzerland)
oaire.citation.volume12
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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