ORIGINAL RESEARCH article
Front. Med.
Sec. Gastroenterology
This article is part of the Research TopicAdvancing Gastrointestinal Disease Diagnosis with Interpretable AI and Edge Computing for Enhanced Patient CareView all 15 articles
Application of Convolutional Neural Networks for Automated Segmentation and Classification in Esophageal Diseases
Provisionally accepted
- First Affiliated Hospital, Nanjing Medical University, Nanjing, China
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Objective: To develop a convolutional neural network (CNN) framework for the automated segmentation and classification of esophageal lesions in endoscopic images. Methods: (1) Lesion localization was performed using a Region-based Convolutional Neural Network (R-CNN). (2) A dual-stream Esophageal Lesion Network (ELNet) was developed to classify images into four diagnostic categories. (3) Lesion segmentation was carried out using an ensemble of three U-Net architectures. Results: The dual-stream ELNet achieved a classification accuracy of 92.14%, with 97.1% specificity and 88.74% sensitivity. The segmentation module based on U-Net attained an overall accuracy of 95.54% and a lesion segmentation sensitivity of 82.89%. The dual-stream ELNet consistently outperformed single-stream baseline networks, and the integrated segmentation-with-classification architecture demonstrated enhanced adaptability across diverse lesion types. Conclusion: The proposed CNN framework enables accurate, robust, and simultaneous classification and segmentation of esophageal endoscopic lesions, exhibiting high performance and clinical potential.
Keywords: artificial intelligence, Convolutional Neural Network, deep learning, EndoscopicImages, Esophageal lesions
Received: 08 Nov 2025; Accepted: 16 Feb 2026.
Copyright: © 2026 Pu, Wang, Yan, Zhuang and He. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Shuaishuai Zhuang
Xiaopu He
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