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Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning

Colonoscopy screening and colonoscopic polypectomy can decrease the incidence and mortality rate of colorectal cancer (CRC). The adenoma detection rate and accuracy of diagnosis of colorectal polyp which vary in different experienced endoscopists have impact on the colonoscopy protection effect of C...

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Autores principales: Hsu, Chen-Ming, Hsu, Chien-Chang, Hsu, Zhe-Ming, Shih, Feng-Yu, Chang, Meng-Lin, Chen, Tsung-Hsing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470682/
https://www.ncbi.nlm.nih.gov/pubmed/34577209
http://dx.doi.org/10.3390/s21185995
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author Hsu, Chen-Ming
Hsu, Chien-Chang
Hsu, Zhe-Ming
Shih, Feng-Yu
Chang, Meng-Lin
Chen, Tsung-Hsing
author_facet Hsu, Chen-Ming
Hsu, Chien-Chang
Hsu, Zhe-Ming
Shih, Feng-Yu
Chang, Meng-Lin
Chen, Tsung-Hsing
author_sort Hsu, Chen-Ming
collection PubMed
description Colonoscopy screening and colonoscopic polypectomy can decrease the incidence and mortality rate of colorectal cancer (CRC). The adenoma detection rate and accuracy of diagnosis of colorectal polyp which vary in different experienced endoscopists have impact on the colonoscopy protection effect of CRC. The work proposed a colorectal polyp image detection and classification system through grayscale images and deep learning. The system collected the data of CVC-Clinic and 1000 colorectal polyp images of Linkou Chang Gung Medical Hospital. The red-green-blue (RGB) images were transformed to 0 to 255 grayscale images. Polyp detection and classification were performed by convolutional neural network (CNN) model. Data for polyp detection was divided into five groups and tested by 5-fold validation. The accuracy of polyp detection was 95.1% for grayscale images which is higher than 94.1% for RGB and narrow-band images. The diagnostic accuracy, precision and recall rates were 82.8%, 82.5% and 95.2% for narrow-band images, respectively. The experimental results show that grayscale images achieve an equivalent or even higher accuracy of polyp detection than RGB images for lightweight computation. It is also found that the accuracy of polyp detection and classification is dramatically decrease when the size of polyp images small than 1600 pixels. It is recommended that clinicians could adjust the distance between the lens and polyps appropriately to enhance the system performance when conducting computer-assisted colorectal polyp analysis.
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spelling pubmed-84706822021-09-27 Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning Hsu, Chen-Ming Hsu, Chien-Chang Hsu, Zhe-Ming Shih, Feng-Yu Chang, Meng-Lin Chen, Tsung-Hsing Sensors (Basel) Article Colonoscopy screening and colonoscopic polypectomy can decrease the incidence and mortality rate of colorectal cancer (CRC). The adenoma detection rate and accuracy of diagnosis of colorectal polyp which vary in different experienced endoscopists have impact on the colonoscopy protection effect of CRC. The work proposed a colorectal polyp image detection and classification system through grayscale images and deep learning. The system collected the data of CVC-Clinic and 1000 colorectal polyp images of Linkou Chang Gung Medical Hospital. The red-green-blue (RGB) images were transformed to 0 to 255 grayscale images. Polyp detection and classification were performed by convolutional neural network (CNN) model. Data for polyp detection was divided into five groups and tested by 5-fold validation. The accuracy of polyp detection was 95.1% for grayscale images which is higher than 94.1% for RGB and narrow-band images. The diagnostic accuracy, precision and recall rates were 82.8%, 82.5% and 95.2% for narrow-band images, respectively. The experimental results show that grayscale images achieve an equivalent or even higher accuracy of polyp detection than RGB images for lightweight computation. It is also found that the accuracy of polyp detection and classification is dramatically decrease when the size of polyp images small than 1600 pixels. It is recommended that clinicians could adjust the distance between the lens and polyps appropriately to enhance the system performance when conducting computer-assisted colorectal polyp analysis. MDPI 2021-09-07 /pmc/articles/PMC8470682/ /pubmed/34577209 http://dx.doi.org/10.3390/s21185995 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hsu, Chen-Ming
Hsu, Chien-Chang
Hsu, Zhe-Ming
Shih, Feng-Yu
Chang, Meng-Lin
Chen, Tsung-Hsing
Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning
title Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning
title_full Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning
title_fullStr Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning
title_full_unstemmed Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning
title_short Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning
title_sort colorectal polyp image detection and classification through grayscale images and deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470682/
https://www.ncbi.nlm.nih.gov/pubmed/34577209
http://dx.doi.org/10.3390/s21185995
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