The role and impact of deep learning methods in computer-aided diagnosis using gastrointestinal endoscopy

Xuejiao Pang, Zijian Zhao, Ying Weng

Research output: Journal PublicationReview articlepeer-review

3 Citations (Scopus)

Abstract

At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.

Original languageEnglish
Article number694
JournalDiagnostics
Volume11
Issue number4
DOIs
Publication statusPublished - Apr 2021
Externally publishedYes

Keywords

  • Artificial intelligence
  • Computer-aided diagnosis system
  • Deep learning
  • Esophageal lesion
  • Gastric lesion
  • Gastrointestinal endoscopy
  • Intestinal lesion

ASJC Scopus subject areas

  • Clinical Biochemistry

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