IRO Journals
Journal of Innovative Image Processing

An Interpretable Deep Learning Framework for Distinguishing Esophagitis from Barrett’s Esophagus Using Endoscopic Images

Open Access
Published: 21 July, 2026
Pages: 1170-1195
  • Atheequllah Khan M. , Atheequllah Khan M.
    Research Scholar, Department of Computer Science and Engineering, Annamacharya University, Rajampet, India
    Research Scholar, Department of Computer Science and Engineering, Annamacharya University, Rajampet, India
  • Krishna J. Krishna J.
    Associate Professor, Department of Artificial Intelligence and Machine Learning, Annamacharya University, Rajampet, India
    Associate Professor, Department of Artificial Intelligence and Machine Learning, Annamacharya University, Rajampet, India
view PDF
view PDF

How to Cite

M., Atheequllah Khan, and Krishna J. 2026. “An Interpretable Deep Learning Framework for Distinguishing Esophagitis from Barrett’s Esophagus Using Endoscopic Images”. Journal of Innovative Image Processing 8 (3): 1170-95. https://doi.org/10.36548/jiip.2026.3.020.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

Esophagitis
Barrett’s Esophagus
Endoscopic Image Analysis
Deep Learning
Convolutional Neural Network
Transfer Learning
End-to-End Fine-Tuning
Explainable AI
Grad-CAM
Computer-Aided Diagnosis

Abstract

The accurate distinction between esophagitis and Barrett's Esophagus is important for prompt diagnosis and clinical management, since Barrett's Esophagus is known to be a precursor of esophageal adenocarcinoma. However, the endoscopic appearance is difficult to interpret, as the overall attachment and appearance of the mucosa are similar and can be interpreted differently among clinicians. The aim of this work is to present an interpretable deep learning framework to automatically classify endoscopic images of esophagitis or Barrett's Esophagus into either binary class. The framework was validated on a balanced set, which has been built from the publicly available Kvasir and HyperKvasir databases. To gain insight into the model transparency, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to display the regions of the image that were more relevant for the classification decision. Three-fold stratified cross-validation (CV) was used to evaluate the experimental results and the fine-tuned CNNs consistently outperformed the feature extraction based methods. The most recent model that was evaluated was fine-tuned InceptionV3 with an accuracy of 99.80%, a precision of 99.60%, a recall of 100.00%, an F1-score of 99.80%, and an area under the receiver operating characteristic curve (AUC) of 1.000. The model was found to be more interpretable, as shown by Grad-CAM, which indicated that the model learned about clinically relevant mucosal abnormalities. The results of this study support the potential of the proposed framework as an accurate and interpretable computer-aided diagnostic tool for assessment of esophageal diseases in endoscopy.

References

  1. Ebigbo, Alanna, Helmut Messmann, and Sung Hak Lee. "Artificial Intelligence Applications in Image-Based Diagnosis of Early Esophageal and Gastric Neoplasms." Gastroenterology (2025) 396-415.
  2. Ma, Yikun, Bo Li, Ying Chen, Zijie Yue, Shuchang Xu, Jingyao Li, Lei Ma et al. "Development and Validation of an AI Foundation Model for Endoscopic Diagnosis of Esophagogastric Junction Adenocarcinoma: A Cohort and Deep Learning Study." Eclinicalmedicine 89 (2025).
  3. Tabassum, Sumaiya, Md Faysal Ahamed, Hafsa Binte Kibria, Md Nahiduzzaman, Julfikar Haider, Muhammad EH Chowdhury, and Mohammad Tariqul Islam. "GastroViT: A Vision Transformer Based Ensemble Learning Approach for Gastrointestinal Disease Classification with Grad CAM & SHAP Visualization." arXiv preprint arXiv:2509.26502 (2025).
  4. Zhuang, Huangming, Jixiang Zhang, and Fei Liao. "A Systematic Review on Application of Deep Learning in Digestive System Image Processing." The Visual Computer 39, no. 6 (2023): 2207-2222.
  5. Alharbe, Nawaf R., Raafat M. Munshi, Manal M. Khayyat, Mashael M. Khayyat, Saadia Hassan Abdalaha Hamza, and Abeer A. Aljohani. "Research Article Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model." (2022).
  6. Gehrung, Marcel, Mireia Crispin-Ortuzar, Adam G. Berman, Maria O’Donovan, Rebecca C. Fitzgerald, and Florian Markowetz. "Triage-Driven Diagnosis of Barrett’s Esophagus for Early Detection of Esophageal Adenocarcinoma Using Deep Learning." Nature medicine 27, no. 5 (2021): 833-841.
  7. Mascarenhas, Miguel, Francisco Mendes, Miguel Martins, Tiago Ribeiro, Joao Afonso, Pedro Cardoso, João Ferreira, João Fonseca, and Guilherme Macedo. "Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy." Journal of Clinical Medicine 14, no. 2 (2025): 549.
  8. Aalam, Syed Wajid, Abdul Basit Ahanger, Tariq A. Masoodi, Ajaz A. Bhat, Ammira S. Al-Shabeeb Akil, Meraj Alam Khan, Assif Assad, Muzafar A. Macha, and Muzafar Rasool Bhat. "Deep Learning-Based Identification of Esophageal Cancer Subtypes Through Analysis of High-Resolution Histopathology Images." Frontiers in Molecular Biosciences 11 (2024): 1346242.
  9. Jiang, Guozhong, Zhizhong Wang, Zhenguo Cheng, Weiwei Wang, Shuangshuang Lu, Zifang Zhang, Chinedu A. Anene et al. "The Integrated Molecular and Histological Analysis Defines Subtypes of Esophageal Squamous Cell Carcinoma." Nature communications 15, no. 1 (2024): 8988.
  10. Tomita, Naofumi, Behnaz Abdollahi, Jason Wei, Bing Ren, Arief Suriawinata, and Saeed Hassanpour. "Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides." JAMA network open 2, no. 11 (2019): e1914645.
  11. Zhang, Peipei, Yifei She, Junfeng Gao, Zhaoyan Feng, Qinghai Tan, Xiangde Min, and Shengzhou Xu. "Development of a Deep Learning System to Detect Esophageal Cancer by Barium Esophagram." Frontiers in Oncology 12 (2022): 766243.
  12. Shiroma, Sho, Toshiyuki Yoshio, Yusuke Kato, Yoshimasa Horie, Ken Namikawa, Yoshitaka Tokai, Shoichi Yoshimizu et al. "Ability of Artificial Intelligence to Detect T1 Esophageal Squamous Cell Carcinoma from Endoscopic Videos and the Effects of Real-Time Assistance." Scientific Reports 11, no. 1 (2021): 7759.
  13. Gao, Xin, Jiaxi Lin, Changju Qu, Chao Wang, Airong Wu, Jinzhou Zhu, and Chunfang Xu. "Computer-Aided Diagnostic System with Automated Deep Learning Method Based on the AutoGluon Framework Improved the Diagnostic Accuracy of Early Esophageal Cancer." Journal of gastrointestinal oncology 15, no. 2 (2024): 535-543.
  14. Chen, Kuan-bing, Ying Xuan, Ai-jun Lin, and Shao-hua Guo. "Esophageal Cancer Detection Based on Classification of Gastrointestinal CT Images Using Improved Faster RCNN." Computer Methods and Programs in Biomedicine 207 (2021): 106172.
  15. Aftab, Muhammad, Faisal Mehmood, Kashif Iqbal Sahibzada, Chengjuan Zhang, Yanan Jiang, and Kangdong Liu. "Attention-Enhanced Multi-Task Deep Learning Model for Classification and Segmentation of Esophageal Lesions." ACS omega 10, no. 10 (2025): 10468-10479.
  16. Xu, Jiayang, Chen Huang, Qianshun Chen, Jieyang Wang, Yuyu Lin, Wei Tang, Wei Shen, and Xunyu Xu. "Tumor–Lymph Cross-Plane Projection Reveals Spatial Relationship Features: A ResNet-CBAM Model for Prognostic Prediction in Esophageal Cancer." Frontiers in Oncology 15 (2025): 1567238.
  17. Takeda, Tsutomu, Daisuke Asaoka, Hiroya Ueyama, Daiki Abe, Maiko Suzuki, Yoshihiro Inami, Yasuko Uemura et al. "Development of an Artificial Intelligence Diagnostic System Using Linked Color Imaging for Barrett’s Esophagus." Journal of Clinical Medicine 13, no. 7 (2024): 1990.
  18. Boro, Dipika, Qilei Chen, Xiaolong Liang, Yu Cao, and Benyuan Liu. "EndoMAE: a Foundation Model for Endoscopic Image Analysis via Self‑Supervised Learning." In Medical Imaging 2026: Computer-Aided Diagnosis, vol. 13926, SPIE, 2026, 625-634.
  19. Friedetzki, Tobias, Naveen Chandraiah, Emil Svoboda, Pavel Pecina, Frank Puppe, and Adrian Krenzer. "Discriminative Self-Supervised Pre-Training for Esophagitis Detection in Upper GI Endoscopy Images." In Medical Imaging with Deep Learning. 2026.
  20. Pogorelov, Konstantin, Kristin Ranheim Randel, Carsten Griwodz, Sigrun Losada Eskeland, Thomas de Lange, Dag Johansen, Concetto Spampinato et al. "Kvasir: A multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection." In Proceedings of the 8th ACM on Multimedia Systems Conference, 2017, 164-169.
  21. Borgli, Hanna, Vajira Thambawita, Pia H. Smedsrud, Steven Hicks, Debesh Jha, Sigrun L. Eskeland, Kristin Ranheim Randel et al. "HyperKvasir, a Comprehensive Multi-Class Image and Video Dataset for Gastrointestinal Endoscopy." Scientific data 7, no. 1 (2020): 283.
  22. Massironi, Sara. "Advancements in Barrett's Esophagus Detection: The Role of Artificial Intelligence and Its Implications." World Journal of Gastroenterology 30, no. 11 (2024): 1494.
  23. Bouzid, Kenza, Harshita Sharma, Sarah Killcoyne, Daniel C. Castro, Anton Schwaighofer, Max Ilse, Valentina Salvatelli et al. "Enabling Large-Scale Screening of Barrett’s Esophagus Using Weakly Supervised Deep Learning in Histopathology." Nature Communications 15, no. 1 (2024): 2026.
  24. Wei, Wei, Xiao-Lei Zhang, Hong-Zhen Wang, Lin-Lin Wang, Jing-Li Wen, Xin Han, and Qian Liu. "Application of Deep Learning Models in the Pathological Classification and Staging of Esophageal Cancer: A Focus on Wave-Vision Transformer." World Journal of Gastroenterology 31, no. 19 (2025): 104897.