Feng XY, Xu X, Zhang Y, Xu YM, She Q, Deng B. Application of convolutional neural network in detecting and classifying gastric cancer.
Artif Intell Gastrointest Endosc 2021;
2:71-78. [DOI:
10.37126/aige.v2.i3.71]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/21/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer (GC) is the fifth most common cancer in the world, and at present, esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC. Convolutional neural networks (CNNs) are a type of deep learning model and have been widely used for image analysis. This paper reviews the application and prospects of CNNs in detecting and classifying GC, aiming to introduce a computer-aided diagnosis system and to provide evidence for subsequent studies.
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