IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, erro...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 23; číslo 18; s. 7724
Hlavní autori:	Ahamed, Md. Faysal, Syfullah, Md. Khalid, Sarkar, Ovi, Islam, Md. Tohidul, Nahiduzzaman, Md, Islam, Md. Rabiul, Khandakar, Amith, Ayari, Mohamed Arselene, Chowdhury, Muhammad E. H.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 01.09.2023 MDPI
Predmet:	Accuracy Algorithms Area Under Curve Automation Benchmarking Colonoscopy Colorectal cancer CVC-ClinicDB Data mining Datasets Deep Learning Diagnosis Image Processing, Computer-Assisted IRv2-Net Kvasir-SEG Medical imaging equipment Medical research Medical screening Methods Polyps segmentation Semantics Surveillance test time augmentation Germany Taiwan test time augmentation polyps IRv2-Net Kvasir-SEG segmentation CVC-ClinicDB colonoscopy
ISSN:	1424-8220, 1424-8220
On-line prístup:	Získať plný text
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