Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy
Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Korean Society of Gastrointestinal Endoscopy
2020-03-01
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| Series: | Clinical Endoscopy |
| Subjects: | |
| Online Access: | http://www.e-ce.org/upload/pdf/ce-2020-054.pdf |
| _version_ | 1797426344194211840 |
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| author | Joonmyeong Choi Keewon Shin Jinhoon Jung Hyun-Jin Bae Do Hoon Kim Jeong-Sik Byeon Namku Kim |
| author_facet | Joonmyeong Choi Keewon Shin Jinhoon Jung Hyun-Jin Bae Do Hoon Kim Jeong-Sik Byeon Namku Kim |
| author_sort | Joonmyeong Choi |
| collection | DOAJ |
| description | Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology. |
| first_indexed | 2024-03-09T08:28:52Z |
| format | Article |
| id | doaj.art-8010c34373f7451aa6388b45833d752f |
| institution | Directory Open Access Journal |
| issn | 2234-2400 2234-2443 |
| language | English |
| last_indexed | 2024-03-09T08:28:52Z |
| publishDate | 2020-03-01 |
| publisher | Korean Society of Gastrointestinal Endoscopy |
| record_format | Article |
| series | Clinical Endoscopy |
| spelling | doaj.art-8010c34373f7451aa6388b45833d752f2023-12-02T20:36:43ZengKorean Society of Gastrointestinal EndoscopyClinical Endoscopy2234-24002234-24432020-03-0153211712610.5946/ce.2020.0547342Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for EndoscopyJoonmyeong Choi0Keewon Shin1Jinhoon Jung2Hyun-Jin Bae3Do Hoon Kim4Jeong-Sik Byeon5Namku Kim6 Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Korea Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Korea Promedius, Inc., Seoul, Korea Promedius, Inc., Seoul, Korea Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, KoreaRecently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.http://www.e-ce.org/upload/pdf/ce-2020-054.pdfartificial intelligenceconvolutional neural networkdeep learningendoscopic imagingmachine learning |
| spellingShingle | Joonmyeong Choi Keewon Shin Jinhoon Jung Hyun-Jin Bae Do Hoon Kim Jeong-Sik Byeon Namku Kim Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy Clinical Endoscopy artificial intelligence convolutional neural network deep learning endoscopic imaging machine learning |
| title | Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy |
| title_full | Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy |
| title_fullStr | Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy |
| title_full_unstemmed | Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy |
| title_short | Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy |
| title_sort | convolutional neural network technology in endoscopic imaging artificial intelligence for endoscopy |
| topic | artificial intelligence convolutional neural network deep learning endoscopic imaging machine learning |
| url | http://www.e-ce.org/upload/pdf/ce-2020-054.pdf |
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