Polyp Segmentation in Colonoscopy Images using U-Net and Cyclic Learning Rate
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Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Colonoscopy 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.
Açıklama
International Conference on Decision Aid Sciences and Applications (DASA) -- MAR 23-25, 2022--AG289 Chiangrai, THAILAND
Anahtar Kelimeler
Polyp segmentation, Cancer detection, Colonoscopy, Deep learning, Cyclical Learning Rates
Kaynak
2022 International Conference On Decision Aid Sciences and Applications (Dasa)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
