Development of anatomical classification models for upper gastrointestinal endoscopy based on deep convolutional neural networks(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2023年第8期
- Page:
- 1051-1056
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Development of anatomical classification models for upper gastrointestinal endoscopy based on deep convolutional neural networks
- Author(s):
- XU Guoting1; 2; WU Airong1; 2; LIN Jiaxi1; 2; GAO Xin1; 2; ZHOU Xin3; GU Huiyuan1; 2; XU Chunfang1; 2; ZHU Jinzhou1; 2
- 1. Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou 215000, China 2. Suzhou Clinical Center of Digestive Disease, Suzhou 215000, China 3. Department of Gastroenterology, Jintan Hospital Affiliated to Jiangsu University, Changzhou 213200, China
- Keywords:
- Keywords upper gastrointestinal tract gastroscope anatomical location deep convolutional neural network model construction
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2023.08.021
- Abstract:
- Abstract: Objective To develop anatomical classification models for upper gastrointestinal endoscopy using deep convolutional neural networks. Methods: A total of 4 183 gastroscopic images collected from the Gastrointestinal Endoscopy Center of the First Affiliated Hospital of Soochow University were randomly divided into training set and validation set at a ratio of 8:2, while 270 gastroscopic images from Jintan Hospital Affiliated to Jiangsu University were collected as the test set. The anatomical structures (esophagus, cardia, gastric fundus, gastric body, gastric angle, gastric antrum, pylorus, duodenal bulb and descending) were labeled in the gastroscopic images. Three deep convolutional neural networks, namely Xception, NASNet Large (NASNet) and ResNet50V2 (ResNet), which had been pre-trained in ImageNet database, were trained in training set and validation set for constructing the anatomical classification models for upper gastrointestinal endoscopy. The gradient-weighted class activation mapping was used to visually interpret the classification results of the models, and the classification abilities of the models were evaluated in validation set and test set. Results Three anatomical classification models for upper gastrointestinal endoscopy based on deep convolutional neural network were successfully constructed. All models had high classification ability. In the validation set, the average classification accuracy, recall and precision were 0.980, 0.894 and 0.920, respectively. Among them, ResNet model performed best, with the highest classification accuracy (0.982), classification recall (0.905) and classification precision (0.933). In the test set, the average classification accuracy, recall and precision were 0.988, 0.942 and 0.950, respectively. Among them, NASNet model performed best, with the highest classification accuracy (0.992), classification recall (0.959) and classification precision (0.970). The gradient-weighted class activation mapping provides a visual interpretation of the model classification results in the form of thermal map. Conclusion: The anatomical classification model developed by deep convolutional neural network for upper gastrointestinal endoscopy has preferable classification ability.
Last Update: 2023-09-06