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

Authors
 Hong Jin Yoon  ;  Jie-Hyun Kim 
Citation
 CLINICAL ENDOSCOPY, Vol.53(2) : 127-131, 2020-03 
Journal Title
 CLINICAL ENDOSCOPY 
ISSN
 2234-2400 
Issue Date
2020-03
Keywords
Artificial intelligence ; Convolutional neural networks ; Early gastric cancer ; Endoscopy ; Invasion depth
Abstract
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.
Files in This Item:
T202001319.pdf Download
DOI
10.5946/ce.2020.046
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jie-Hyun(김지현) ORCID logo https://orcid.org/0000-0002-9198-3326
Yoon, Hong Jin(윤홍진) ORCID logo https://orcid.org/0000-0002-4880-3262
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/176094
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