Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network
Abstract
The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal...
Paper Details
Title
Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network
Published Date
May 3, 2021
Journal
Volume
12
Issue
6
Pages
3066 - 3066
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