Polyp Segmentation in Colonoscopy Images using U-Net and Cyclic Learning Rate

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dc.contributor.authorBulut, Betul
dc.contributor.authorButun, Ertan
dc.contributor.authorKaya, Mehmet
dc.date.accessioned2025-03-17T12:25:39Z
dc.date.available2025-03-17T12:25:39Z
dc.date.issued2022
dc.departmentTarsus Üniversitesi
dc.descriptionInternational Conference on Decision Aid Sciences and Applications (DASA) -- MAR 23-25, 2022--AG289 Chiangrai, THAILAND
dc.description.abstractColonoscopy is an important procedure in the diagnosis of colorectal cancer. The use of computer aided systems has become important to support clinicians performing colonoscopy and to prevent polyps from escaping the clinician's attention. Image segmentation studies using deep learning achieves successful results and can play a crucial role on diagnosis procedure of colorectal cancer. We trained Unet architecture for polyp segmentation and determined the learning rate, one of the most important training parameters, using Cyclic Learning Rate policy. The results show that the success rate is increased in the segmentation task performed Unet with Cyclic Learning Rate policy. In this study, we have contributed to more accurate detection of polyp diagnosis, which can be a precursor to cancer, by using the UNET architecture with an effective learning rate strategy.
dc.identifier.doi10.1109/DASA54658.2022.9765101
dc.identifier.endpage1152
dc.identifier.isbn978-1-6654-9501-1
dc.identifier.scopus2-s2.0-85130150964
dc.identifier.scopusqualityN/A
dc.identifier.startpage1149
dc.identifier.urihttps://doi.org/10.1109/DASA54658.2022.9765101
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1801
dc.identifier.wosWOS:000839386600024
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee
dc.relation.ispartof2022 International Conference On Decision Aid Sciences and Applications (Dasa)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectPolyp segmentation
dc.subjectCancer detection
dc.subjectColonoscopy
dc.subjectDeep learning
dc.subjectCyclical Learning Rates
dc.titlePolyp Segmentation in Colonoscopy Images using U-Net and Cyclic Learning Rate
dc.typeConference Object

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