Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy
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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 sufferedfrom overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing tothe enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithmsfor deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computervision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcareapplications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, andchallenges of deep-learning technology.
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 sufferedfrom overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing tothe enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithmsfor deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computervision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcareapplications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, andchallenges of deep-learning technology.
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