Establishment and Clinical Test of Automatic Image Recognition Model for Ulcerative Colitis Colonoscopy Based on ResNet
Objective To train an automatic recognition and classification model of ulcerative colitis colonoscopy image based on ResNet,and to test its accuracy,in order to help doctors improve the clinical detection rate and classification accuracy of ulcerative colitis.Methods A total of 4000 colonoscopy images were retrospectively collected from the Colonoscopy Center of Hebei Hospital of Traditional Chinese Medicine from January 2018 to October 2023,and were divided into normal group,mild group,moderate group and severe group according to Mayo endoscopic scoring criteria,with 1000 images for each group.After pre-processing such as brightness adjustment and Angle rotation,the number of images was expanded to 20,000,and the data set was randomly divided into training set,verification set and test set according to the ratio of 7∶2∶1.The training set and verification set are input into the ResNet model to learn and test its stability.After all training is completed,the accuracy of the model is recorded through the test set,and the accurate regression curve is made to evaluate the classification effect of the model.Results In the test set,the accuracy of classification of ulcerative colitis was 99.8％in normal group,98.8％in mild group,95.6％in moderate group and 97.8％in severe group.Conclusion ResNet has good performance in image recognition and classification of ulcerative colitis,can improve the clinical accuracy of ulcerative colitis,and can assist doctors to identify and classify the disease,which has a more reliable clinical application value.
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