Deep learning models for the classification of Mayo endoscopic score of ulcerative colitis
10.3760/cma.j.cn101480-20230322-00038
- VernacularTitle:基于深度学习的溃疡性结肠炎Mayo内镜评分模型的建立
- Author:
Chang XU
1
;
Jiaxi LIN
;
Yu WANG
;
Jianying LU
;
Xiaolin LIU
;
Chunfang XU
;
Jinzhou ZHU
Author Information
1. 苏州大学附属第一医院消化内科，苏州 215006
- Publication Type:Journal Article
- Keywords:
Endoscopy;
Ulcerative colitis;
Deep learning;
Convolutional neural network
- From:
Chinese Journal of Inflammatory Bowel Diseases
2024;08(1):71-76
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop deep learning models for ulcerative colitis (UC) classification based on Mayo endoscopic score.Methods:A total of 2400 endoscopic images from the Gastrointestinal Endoscopy Centre of the First Affiliated Hospital of Soochow University and the HyperKvasir database were extracted for training classification models, and 200 endoscopic images from Affiliated Jintan Hospital of Jiangsu University were extracted for evaluating the models, both scored by endoscopists according to Mayo endoscopic score (score 0-3). Four deep convolutional neural networks (MobileNetV2, ResNetV2, Xception, EfficientNetV2S), which were pre-trained in the ImageNet database, were used to develop the UC classification models by transfer learning. Models were evaluated in the test set based on the confusion matrix using accuracy, Matthews correlation coefficient (MCC) and Cohen′s kappa, and compared with the performance of senior and junior physicians. Meanwhile, the model was visualized by gradient-weighted class activation mapping.Results:Four deep learning Mayo score models based on UC endoscopic image classification models were successfully developed. In the test set, the accuracy of MobileNetV2, ResNetV2, Xception and EfficientNetV2S was 0.785, 0.800, 0.815, 0.830, respectively (average accuracy 0.808). Amoug them, EfficientNetV2S model was the best, higher than junior physician′s accuracy (accuracy 0.785), and slightly lower than senior physician′s (accuracy 0.870) .Conclusions:The UC endoscopic severity classification models developed by deep learning show good performance, which can be further improved by larger sample size and optimizing the framework.
