ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes-Reference-Cited by-同舟云学术

ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

Published:2023 Issue: Volume:11 Page:16142-16155
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ige Ayokunle Olalekan¹^ORCID,Tomar Nikhil Kumar²,Aranuwa Felix Ola³,Oriola Oluwafemi³,Akingbesote Alaba O.³,Noor Mohd Halim Mohd¹^ORCID,Mazzara Manuel⁴^ORCID,Aribisala Benjamin Segun⁵

Affiliation:

1. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia

2. School of Computer and Information Sciences (SOCIS), Indira Gandhi National Open University, New Delhi, India

3. Department of Computer Science, Adekunle Ajasin University, Akungba-Akoko, Ondo, Nigeria

4. Institute of Software Development and Engineering, Innopolis University, Innopolis, Russia

5. Department of Computer Science, Lagos State University, Lagos, Nigeria

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10043855.pdf?arnumber=10043855

Reference50 articles.

1. Use of Artificial Intelligence-Based Analytics From Live Colonoscopies to Optimize the Quality of the Colonoscopy Examination in Real Time: Proof of Concept

2. Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

3. PolypSeg: An Efficient Context-Aware Network for Polyp Segmentation from Colonoscopy Videos

4. Automatic Polyp Segmentation Using Convolutional Neural Networks

5. Polyp Miss Rate Determined by Tandem Colonoscopy: A Systematic Review

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