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Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer

Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it...

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Detalles Bibliográficos
Autores principales: Yoon, Hong Jin, Kim, Jie-Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Gastrointestinal Endoscopy 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7137575/
https://www.ncbi.nlm.nih.gov/pubmed/32252505
http://dx.doi.org/10.5946/ce.2020.046
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author Yoon, Hong Jin
Kim, Jie-Hyun
author_facet Yoon, Hong Jin
Kim, Jie-Hyun
author_sort Yoon, Hong Jin
collection PubMed
description Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it is important to determine the optimal method of applying CNN for each organ and disease. Lesion-based CNN is a type of deep learning model designed to learn the entire lesion from endoscopic images. This review describes the application of lesion-based CNN technology in diagnosis of EGC.
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spelling pubmed-71375752020-04-10 Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer Yoon, Hong Jin Kim, Jie-Hyun Clin Endosc Focused Review Series: Application of Artificial Intelligence in GI Endoscopy Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it is important to determine the optimal method of applying CNN for each organ and disease. Lesion-based CNN is a type of deep learning model designed to learn the entire lesion from endoscopic images. This review describes the application of lesion-based CNN technology in diagnosis of EGC. Korean Society of Gastrointestinal Endoscopy 2020-03 2020-03-30 /pmc/articles/PMC7137575/ /pubmed/32252505 http://dx.doi.org/10.5946/ce.2020.046 Text en Copyright © 2020 Korean Society of Gastrointestinal Endoscopy This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Focused Review Series: Application of Artificial Intelligence in GI Endoscopy
Yoon, Hong Jin
Kim, Jie-Hyun
Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer
title Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer
title_full Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer
title_fullStr Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer
title_full_unstemmed Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer
title_short Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer
title_sort lesion-based convolutional neural network in diagnosis of early gastric cancer
topic Focused Review Series: Application of Artificial Intelligence in GI Endoscopy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7137575/
https://www.ncbi.nlm.nih.gov/pubmed/32252505
http://dx.doi.org/10.5946/ce.2020.046
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