Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

Overview

Journal Clin Endosc

Date 2020 Apr 8

PMID 32252504

Citations 22

Authors

Joonmyeong Choi

Keewon Shin

Jinhoon Jung

Hyun-Jin Bae

Do Hoon Kim

Jeong-Sik Byeon

Namku Kim

Affiliations

Soon will be listed here.

Abstract

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.

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