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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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 23; no. 18; p. 7724
Main Authors:	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.
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.09.2023 MDPI
Subjects:	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
Online Access:	Get full text
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