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An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy

Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, u...

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Autores principales: Sharma, Pallabi, Balabantaray, Bunil Kumar, Bora, Kangkana, Mallik, Saurav, Kasugai, Kunio, Zhao, Zhongming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9086187/
https://www.ncbi.nlm.nih.gov/pubmed/35559018
http://dx.doi.org/10.3389/fgene.2022.844391
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author Sharma, Pallabi
Balabantaray, Bunil Kumar
Bora, Kangkana
Mallik, Saurav
Kasugai, Kunio
Zhao, Zhongming
author_facet Sharma, Pallabi
Balabantaray, Bunil Kumar
Bora, Kangkana
Mallik, Saurav
Kasugai, Kunio
Zhao, Zhongming
author_sort Sharma, Pallabi
collection PubMed
description Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, unidentified polyps can develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance has not been comparable to an expert endoscopist yet. Here, we propose a multiple classifier consultation strategy to create an effective and powerful classifier for polyp identification. This strategy benefits from recent findings that different classification models can better learn and extract various information within the image. Therefore, our Ensemble classifier can derive a more consequential decision than each individual classifier. The extracted combined information inherits the ResNet’s advantage of residual connection, while it also extracts objects when covered by occlusions through depth-wise separable convolution layer of the Xception model. Here, we applied our strategy to still frames extracted from a colonoscopy video. It outperformed other state-of-the-art techniques with a performance measure greater than 95% in each of the algorithm parameters. Our method will help researchers and gastroenterologists develop clinically applicable, computational-guided tools for colonoscopy screening. It may be extended to other clinical diagnoses that rely on image.
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spelling pubmed-90861872022-05-11 An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy Sharma, Pallabi Balabantaray, Bunil Kumar Bora, Kangkana Mallik, Saurav Kasugai, Kunio Zhao, Zhongming Front Genet Genetics Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, unidentified polyps can develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance has not been comparable to an expert endoscopist yet. Here, we propose a multiple classifier consultation strategy to create an effective and powerful classifier for polyp identification. This strategy benefits from recent findings that different classification models can better learn and extract various information within the image. Therefore, our Ensemble classifier can derive a more consequential decision than each individual classifier. The extracted combined information inherits the ResNet’s advantage of residual connection, while it also extracts objects when covered by occlusions through depth-wise separable convolution layer of the Xception model. Here, we applied our strategy to still frames extracted from a colonoscopy video. It outperformed other state-of-the-art techniques with a performance measure greater than 95% in each of the algorithm parameters. Our method will help researchers and gastroenterologists develop clinically applicable, computational-guided tools for colonoscopy screening. It may be extended to other clinical diagnoses that rely on image. Frontiers Media S.A. 2022-04-26 /pmc/articles/PMC9086187/ /pubmed/35559018 http://dx.doi.org/10.3389/fgene.2022.844391 Text en Copyright © 2022 Sharma, Balabantaray, Bora, Mallik, Kasugai and Zhao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Sharma, Pallabi
Balabantaray, Bunil Kumar
Bora, Kangkana
Mallik, Saurav
Kasugai, Kunio
Zhao, Zhongming
An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy
title An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy
title_full An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy
title_fullStr An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy
title_full_unstemmed An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy
title_short An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy
title_sort ensemble-based deep convolutional neural network for computer-aided polyps identification from colonoscopy
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9086187/
https://www.ncbi.nlm.nih.gov/pubmed/35559018
http://dx.doi.org/10.3389/fgene.2022.844391
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