Preparing Well for Esophageal Endoscopic Detection Using a Hybrid Model and Transfer Learning.

ABSTRACT: Early detection of esophageal cancer through endoscopic imaging is pivotal for effective treatment. However, the intricacies of endoscopic diagnosis, contingent on the physician's expertise, pose challenges. Esophageal cancer features often manifest ambiguously, leading to potential confusions with other inflammatory esophageal conditions, thereby complicating diagnostic accuracy. In recent times, computer-aided diagnosis has emerged as a promising solution in medical imaging, particularly within the domain of endoscopy. Nonetheless, contemporary AI-based diagnostic models heavily rely on voluminous data sources, limiting their applicability, especially in scenarios with scarce datasets. To address this limitation, our study introduces novel data training strategies based on transfer learn

SUBMITTER: Chou CK 

PROVIDER: S-EPMC10417640 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature