The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy

Diagnostics

Ying Weng¹  Xuejiao Pang²  Zijian Zhao² 

[1] School of Computer Science, University of Nottingham, Nottingham NG7 2RD, UK;School of Control Science and Engineering, Shandong University, Jinan 250061, China;

关键词: artificial intelligence;    computer-aided diagnosis system;    deep learning;    esophageal lesion;    gastric lesion;    gastrointestinal endoscopy;   

DOI  :  10.3390/diagnostics11040694

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown