Published online Jun 28, 2021. doi: 10.37126/aige.v2.i3.71
Peer-review started: April 27, 2021
First decision: April 28, 2021
Revised: May 21, 2021
Accepted: June 7, 2021
Article in press: June 7, 2021
Published online: June 28, 2021
Processing time: 70 Days and 3.8 Hours
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.
Core Tip: With the development of new algorithms and big data, great achievements in artificial intelligence (AI) based on deep learning have been made in diagnostic imaging, especially convolutional neural network (CNN). Esophagogastroduodenoscopy (EGD) is currently the most common method for screening and diagnosing gastric cancer (GC). When AI was combined with EGD, the diagnostic efficacy of GC could be improved. Therefore, we review the application and prospect of CNN in detecting and classifying GC, aiming to introduce a computer-aided diagnosis system and provide evidence for following studies.