A Novel Implementation of Esophagus Diagnosis Using Deep Learning

Abstract

The presented work is a systematic review on recent technologies in deep learning for Barrett's esophagus (BE), a disease which affects the food pipe. Evaluation of this disorder can be made easy with the help of model developed with deep learning and artificial intelligence. The dysplasia and adenocarcinoma exhibits a complex pattern for detecting through endoscopic diagnosis. The diagnosis and automatic detection using computer analysis is beneficial for assisting the endoscopy procedure. The deep learning technique developed through manual and automated segmentation for the evaluation of BE disorders. The review is done by compilation of works published in Springer, Binda Wi, IEEEXplore, and Association for Computing Machinery, Science Direct and Pubmed on the category of automatic detection of regions for classification purposes. The problem statement, methodology, objective and result of the selected work have been analyzed.