Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Canceropen access
- Authors
- Yoon, Hong Jin; Kim, Jie-Hyun
- Issue Date
- Mar-2020
- Publisher
- 대한소화기내시경학회
- Keywords
- Artificial intelligence; Convolutional neural networks; Early gastric cancer; Endoscopy; Invasion depth
- Citation
- Clinical Endoscopy, v.53, no.2, pp 127 - 131
- Pages
- 5
- Journal Title
- Clinical Endoscopy
- Volume
- 53
- Number
- 2
- Start Page
- 127
- End Page
- 131
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19568
- DOI
- 10.5946/ce.2020.046
- ISSN
- 2234-2400
2234-2443
- 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.
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Collections - College of Medicine > Department of Internal Medicine > 1. Journal Articles
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