A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux Disease

Chi-Chih Wang, Yu-Ching Chiu, Wei-Liang Chen, Tzu-Wei Yang, Ming-Chang Tsai and Ming-Hseng Tseng
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Chi-Chih Wang: Institute of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
Yu-Ching Chiu: Master Program in Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan
Wei-Liang Chen: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
Tzu-Wei Yang: Institute of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
Ming-Chang Tsai: Institute of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
Ming-Hseng Tseng: Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan

IJERPH, 2021, vol. 18, issue 5, 1-14

Abstract: Gastroesophageal reflux disease (GERD) is a common disease with high prevalence, and its endoscopic severity can be evaluated using the Los Angeles classification (LA grade). This paper proposes a deep learning model (i.e., GERD-VGGNet) that employs convolutional neural networks for automatic classification and interpretation of routine GERD LA grade. The proposed model employs a data augmentation technique, a two-stage no-freezing fine-tuning policy, and an early stopping criterion. As a result, the proposed model exhibits high generalizability. A dataset of images from 464 patients was used for model training and validation. An additional 32 patients served as a test set to evaluate the accuracy of both the model and our trainees. Experimental results demonstrate that the best model for the development set exhibited an overall accuracy of 99.2% (grade A–B), 100% (grade C–D), and 100% (normal group) using narrow-band image (NBI) endoscopy. On the test set, the proposed model resulted in an accuracy of 87.9%, which was significantly higher than the results of the trainees (75.0% and 65.6%). The proposed GERD-VGGNet model can assist automatic classification of GERD in conventional and NBI environments and thereby increase the accuracy of interpretation of the results by inexperienced endoscopists.

Keywords: gastroesophageal reflux disease classification; artificial intelligence; deep learning; conventional endoscopy; narrow-band image (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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