Artificial intelligence-assisted diagnosis system of benign and malignant gastric ulcer based on deep learning
10.3760/cma.j.cn321463-20200105-00547
- VernacularTitle:基于深度学习的良恶性胃溃疡人工智能辅助诊断系统研究
- Author:
Li HUANG
1
;
Yanxia LI
;
Lianlian WU
;
Shan HU
;
Yiyun CHEN
;
Jun ZHANG
;
Ping AN
;
Honggang YU
Author Information
1. 武汉大学人民医院消化内科 430060；消化疾病湖北省重点实验室，武汉 430060
- From:
Chinese Journal of Digestive Endoscopy
2020;37(7):476-480
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct an artificial intelligence-assisted diagnosis system to detect gastric ulcer lesions and identify benign and malignant gastric ulcers automatically.Methods:A total of 1 885 endoscopy images were collected from November 2016 to April 2019 in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University. Among them, 636 were normal images, 630 were with benign gastric ulcers, and 619 were with malignant gastric ulcers. A total of 1 735 images belonged to training data set and 150 images were used for validation. These images were input into the Res-net50 model based on the fastai framework, the Res-net50 model based on the Keras framework, and the VGG-16 model based on the Keras framework respectively. Three separate binary classification models of normal gastric mucosa and benign ulcers, normal gastric mucosa and malignant ulcers, and benign and malignant ulcers were constructed.Results:The VGG-16 model showed the best ability of classification. The accuracy of the validation set was 98.0%, 98.0% and 85.0%, respectively, for distinguishing normal gastric mucosa from benign ulcers, normal gastric mucosa from malignant ulcers, and benign ulcers from malignant ulcers.Conclusion:The artificial intelligence-assisted diagnosis system obtained in this study shows noteworthy ability of detection of ulcerous lesions, and is expected to be used in clinical to assist doctors to detect ulcer and identify benign and malignant ulcers.
