Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach.

ABSTRACT:

Background/aims

Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas have failed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI model with inference speeds faster than 25 frames per second that maintains a high level of accuracy.

Methods

Investigators from Chulalongkorn University obtained 802 histological-proven GIM images for AI model training. Four strategies were proposed to improve the model accuracy. First, transfer learning was employed to the public colon datasets. Second, an image preprocessing technique contrast-limited adaptive histogram equalization was employed to produce clearer GIM areas. Third, data augmentation was applied for a more

SUBMITTER: Siripoppohn V 

PROVIDER: S-EPMC9178134 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature