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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Korean Society of Gastrointestinal Endoscopy
2020
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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. |
format | Online Article Text |
id | pubmed-7137575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Korean Society of Gastrointestinal Endoscopy |
record_format | MEDLINE/PubMed |
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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