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

Artificial intelligence-assisted diagnosis system of benign and malignant gastric ulcer based on deep learning

目的:构建人工智能辅助诊断系统,自动发现胃溃疡病灶,鉴别胃良性溃疡与恶性溃疡。方法:收集武汉大学人民医院消化内镜中心2016年11月—2019年4月拍摄的胃镜图片1885张,其中正常胃黏膜图片636张、良性胃溃疡图片630张、恶性胃溃疡图片619张。其中1735张为训练集,150张为测试集,分别将图片输入基于fastai框架的Res-net50模型、基于Keras框架的Res-net50模型和基于Keras框架的VGG-16模型进行训练。分别构建正常胃黏膜与良性溃疡、正常胃黏膜与恶性溃疡、良性与恶性溃疡3个单独的二元分类模型。结果:VGG-16模型表现出了最好的结果,验证集验证模型区分正常黏膜与良性溃疡、正常黏膜与恶性溃疡、良性与恶性溃疡的精确度分别为98.0%、98.0%和85.0%。结论:本研究获得的模型在发现溃疡病灶上具有较好的能力,有望应用于临床辅助溃疡病灶检出并鉴别良恶性溃疡。

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 1885 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 1735 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.