Research progress on endoscopic image diagnosis of gastric tumors based on deep learning.
10.7507/1001-5515.202404004
- Author:
Yuan GAO
1
;
Guohui WEI
1
Author Information
1. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P. R.China.
- Publication Type:English Abstract
- Keywords:
Deep learning;
Endoscopic images;
Gastric cancer;
Gastric tumors
- MeSH:
Humans;
Stomach Neoplasms/diagnostic imaging*;
Deep Learning;
Diagnosis, Computer-Assisted/methods*;
Gastroscopy/methods*;
Algorithms
- From:
Journal of Biomedical Engineering
2024;41(6):1293-1300
- CountryChina
- Language:Chinese
-
Abstract:
Gastric tumors are neoplastic lesions that occur in the stomach, posing a great threat to human health. Gastric cancer represents the malignant form of gastric tumors, and early detection and treatment are crucial for patient recovery. Endoscopic examination is the primary method for diagnosing gastric tumors. Deep learning techniques can automatically extract features from endoscopic images and analyze them, significantly improving the detection rate of gastric cancer and serving as an important tool for auxiliary diagnosis. This paper reviews relevant literature in recent years, presenting the application of deep learning methods in the classification, object detection, and segmentation of gastric tumor endoscopic images. In addition, this paper also summarizes several computer-aided diagnosis (CAD) systems and multimodal algorithms related to gastric tumors, highlights the issues with current deep learning methods, and provides an outlook on future research directions, aiming to promote the clinical application of deep learning methods in the endoscopic diagnosis of gastric tumors.
