SpringerLink
Log in

A graph-optimized deep learning framework for recognition of Barrett’s esophagus and reflux esophagitis

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Barrett’s esophagus (BE) recognition is essential for the diagnosis of esophageal adenocarcinoma (EAC), a highly fatal disease when left untreated at earlier stages of cancer development. General endoscopists often face difficulty in distinguishing BE from reflux esophagitis (RE) due to their resemblance in endoscopic images. This paper proposes a novel framework for the diagnosis of BE and RE in endoscopic images, leveraging deep learning techniques. The proposed framework begins with a localization network that removes interference information and enhances image contrast using histogram equalization. Subsequently, a graph-optimized deep learning (GODL) model is designed for the few-shot classification task. This model consists of two branches: a convolutional neural network (CNN) classification part, incorporating a CNN module for feature representation, and an SVM classifier for classification. Additionally, a graph neural network (GNN) branch is included to capture sample relations and utilize them effectively. The proposed model is trained using a meta-learning strategy and evaluated on endoscopic images obtained from both the **angya Hospital private dataset and the Hyper-Kvasir dataset. Experimental results demonstrate that the proposed model achieves a 4-classification recognition accuracy of 93.0%, with macro-precision, macro-recall, and macro-F scores of 93.5%, 92.9%, and 93.2%, respectively. Importantly, our model’s performance is comparable to that of experienced endoscopists. These promising results suggest that the proposed GODL model can serve as a valuable supportive tool for detecting esophageal diseases in clinical settings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

The dataset analysed during the current study are not publicly available due to data privacy but are available from the corresponding author on reasonable request.

References

  1. Coleman HG, **e S-H, Lagergren J (2018) The epidemiology of esophageal adenocarcinoma. Gastroenterology 154(2):390–405

    Article  Google Scholar 

  2. Hvid-Jensen F, Pedersen L, Drewes AM, Sørensen HT, Funch-Jensen P (2011) Incidence of adenocarcinoma among patients with barrett’s esophagus. N Engl J Med 365(15):1375–1383

    Article  Google Scholar 

  3. Shaheen NJ, Falk GW, Iyer PG, Gerson LB (2016) Acg clinical guideline: diagnosis and management of Barrett’s esophagus. Am Coll Gastroenterol 111(1):30–50

    Article  Google Scholar 

  4. Anaparthy R, Sharma P (2014) Progression of barrett oesophagus: role of endoscopic and histological predictors. Nat Rev Gastroenterol Hepatol 11(9):525–534

    Article  Google Scholar 

  5. Maret-Ouda J, Markar SR, Lagergren J (2020) Gastroesophageal reflux disease: a review. Jama 324(24):2536–2547

    Google Scholar 

  6. Mastracci L, Grillo F, Parente P, Unti E, Battista S, Spaggiari P, Campora M, Scaglione G, Fassan M, Fiocca R (2020) Gastro-esophageal reflux disease and barrett’s esophagus: an overview with an histologic diagnostic approach. Pathologica 112(3):117

    Article  Google Scholar 

  7. Sebastianelli L, Benois M, Vanbiervliet G, Bailly L, Robert M, Turrin N, Gizard E, Foletto M, Bisello M, Albanese A et al (2019) Systematic endoscopy 5 years after sleeve gastrectomy results in a high rate of barrett’s esophagus: results of a multicenter study. Obes Surg 29(5):1462–1469

    Article  Google Scholar 

  8. de Souza Jr LA, Passos LA, Mendel R, Ebigbo A, Probst A, Messmann H, Palm C, Papa JP (2020) Assisting barrett’s esophagus identification using endoscopic data augmentation based on generative adversarial networks. Comput Biol Med 126:104029

    Article  Google Scholar 

  9. Mendel R, Ebigbo A, Probst A, Messmann H, Palm C (2017) Barrett’s esophagus analysis using convolutional neural networks. In: Bildverarbeitung Für die Medizin 2017. Springer, pp 80– 85

  10. de Souza Jr LA, Palm C, Mendel R, Hook C, Ebigbo A, Probst A, Messmann H, Weber S, Papa JP (2018) A survey on Barrett’s esophagus analysis using machine learning. Comput Biol Med 96:203–213

    Article  Google Scholar 

  11. Takenaka K, Ohtsuka K, Fujii T, Negi M, Suzuki K, Shimizu H, Oshima S, Akiyama S, Motobayashi M, Nagahori M et al (2020) Development and validation of a deep neural network for accurate evaluation of endoscopic images from patients with ulcerative colitis. Gastroenterology

  12. Gong EJ, Bang CS, Jung K, Kim SJ, Kim JW, Seo SI, Lee U, Maeng YB, Lee YJ, Lee JI et al (2022) Deep-learning for the diagnosis of esophageal cancers and precursor lesions in endoscopic images: A model establishment and nationwide multicenter performance verification study. J Pers Med 12(7):1052

    Article  Google Scholar 

  13. Kusano C, Singh R, Lee YY, Soh YSA, Sharma P, Ho K-Y, Gotoda T (2022) Global variations in diagnostic guidelines for Barrett’s esophagus. Dig Endosc

  14. Ma H, Wang L, Chen Y, Tian L et al (2022) Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis. Saudi J Gastroenterol 28(5):332

    Article  Google Scholar 

  15. Ohmori M, Ishihara R, Aoyama K, Nakagawa K, Iwagami H, Matsuura N, Shichijo S, Yamamoto K, Nagaike K, Nakahara M et al (2020) Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc 91(2):301–309

    Article  Google Scholar 

  16. Faghani S, Codipilly DC, Vogelsang D, Moassefi M, Rouzrokh P, Khosravi B, Agarwal S, Dhaliwal L, Katzka DA, Hagen C et al (2022) Development of a deep learning model for the histological diagnosis of dysplasia in barrett’s esophagus. Gastrointest Endosc

  17. Dumoulin FL, Rodriguez-Monaco FD, Ebigbo A, Steinbrück I (2022) Artificial intelligence in the management of barrett’s esophagus and early esophageal adenocarcinoma. Cancers 14(8):1918

    Article  Google Scholar 

  18. Ghatwary N, Ye X, Zolgharni M (2019) Esophageal abnormality detection using densenet based faster r-cnn with gabor features. IEEE Access 7:84374–84385

    Article  Google Scholar 

  19. Struyvenberg MR, De Groof AJ, van der Putten J, van der Sommen F, Baldaque-Silva F, Omae M, Pouw R, Bisschops R, Vieth M, Schoon EJ et al (2021) A computer-assisted algorithm for narrow-band imaging-based tissue characterization in barrett’s esophagus. Gastrointest Endosc 93(1):89–98

    Article  Google Scholar 

  20. Salehi AW, Khan S, Gupta G, Alabduallah BI, Almjally A, Alsolai H, Siddiqui T, Mellit A (2023) A study of cnn and transfer learning in medical imaging: Advantages, challenges, future scope. Sustainability 15(7):5930

    Article  Google Scholar 

  21. Yao X, Wang X, Wang S-H, Zhang Y-D (2022) A comprehensive survey on convolutional neural network in medical image analysis. Multimed Tools Appl 81(29):41361–41405

    Article  Google Scholar 

  22. Ahmed MB (2020) An efficient algorithm for medical image classification using deep convolutional network: Case of cancer pathology. In: NISS2020

  23. Abraham B, Nair MS (2020) Computer-aided detection of covid-19 from x-ray images using multi-cnn and bayesnet classifier. Biocybern Biomed Eng 40(4):1436–1445

    Article  Google Scholar 

  24. Sali R, Moradinasab N, Guleria S, Ehsan L, Fernandes P, Shah TU, Syed S, Brown DE (2020) Deep learning for whole-slide tissue histopathology classification: A comparative study in the identification of dysplastic and non-dysplastic Barrett’s esophagus. J Pers Med 10(4):141

    Article  Google Scholar 

  25. Li Z, Feng X, Wu Z, Yang C, Bai B, Yang Q (2019) Classification of atrial fibrillation recurrence based on a convolution neural network with svm architecture. IEEE Access 7:77849–77856

    Article  Google Scholar 

  26. Wang Z, Meng Y, Weng F, Chen Y, Lu F, Liu X, Hou M, Zhang J (2020) An effective cnn method for fully automated segmenting subcutaneous and visceral adipose tissue on ct scans. Ann Biomed Eng 48(5)

  27. Wang Z, **ao Y, Li Y, Zhang J, Lu F, Hou M, Liu X (2020) Automatically discriminating and localizing covid-19 from community-acquired pneumonia on chest x-rays. Pattern Recogn 110:107613

    Article  Google Scholar 

  28. Radak M, Lafta HY, Fallahi H (2023) Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies. J Cancer Res Clin Oncol 1–19

  29. Wang Z, **ao Y, Weng F, Li X, Meng Y (2021) R-jaunlab: Automatic multi-class recognition of jaundice on photos of subjects with region annotation networks. J Dig Imaging 9

  30. Murata M, Usami H, Iwahori Y, Aili W (2017) Polyp classification using multiple cnn-svm classifiers from endoscope images. In: The Ninth International Conferences on Pervasive Patterns and Applications, pp. 109– 112

  31. Waikhom L, Patgiri R (2023) A survey of graph neural networks in various learning paradigms: methods, applications, and challenges. Artif Intell Rev 56(7):6295–6364

    Article  Google Scholar 

  32. Li X, Yang X, Ma Z, Xue J-H (2023) Deep metric learning for few-shot image classification: A review of recent developments. Pattern Recognit 109381

  33. Yu T, He S, Song Y-Z, **ang T (2022) Hybrid graph neural networks for few-shot learning. In: Proceedings of the AAAI conference on artificial intelligence, vol. 36. pp 3179– 3187

  34. Kim J, Kim T, Kim S, Yoo CD (2019) Edge-labeling graph neural network for few-shot learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 11– 20

  35. Zhao K, Zhang Z, Jiang B, Tang J (2022) Lglnn: Label guided graph learning-neural network for few-shot learning. Neural Netw 155:50–57

    Article  Google Scholar 

  36. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770– 778

  37. Qilong W, Banggu W, Pengfei Z, Peihua L, Wangmeng Z, Qinghua H (2020) Eca-net: efficient channel attention for deep convolutional neural networks 2020 ieee. In: CVF Conference on Computer Vision and Pattern Recognition (CVPR)

  38. Guo Y, Ma Z, Li X, Dong Y (2021) Tlrm: Task-level relation module for gnn-based few-shot learning. In: 2021 International Conference on Visual Communications and Image Processing (VCIP). IEEE, pp 1– 5

  39. Borgli H, Thambawita V, Smedsrud PH, Hicks S, Jha D, Eskeland SL, Randel KR, Pogorelov K, Lux M, Nguyen DTD et al (2020) Hyperkvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Scientific Data 7(1):1–14

    Article  Google Scholar 

  40. He K, Gkioxari G, Dollár P, Girshick R (2018) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp 2961– 2969

  41. Cai L, Long T, Dai Y, Huang Y (2020) Mask r-cnn-based detection and segmentation for pulmonary nodule 3d visualization diagnosis. IEEE Access 8:44400–44409

    Article  Google Scholar 

  42. Yu X, Pang W, Xu Q, Liang M (2020) Mammographic image classification with deep fusion learning. Sci Rep 10(1):1–11

    Google Scholar 

  43. Jenifer S, Parasuraman S, Kadirvelu A (2016) Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm. Appl Soft Comput 42:167–177

    Article  Google Scholar 

  44. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ar**v:1409.1556

  45. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2818– 2826

  46. Yang L, Li L, Zhang Z, Zhou X, Zhou E, Liu Y (2020) Dpgn: Distribution propagation graph network for few-shot learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 13390– 13399

  47. Mubarak D et al (2022) Classification of early stages of esophageal cancer using transfer learning. IRBM 43(4):251–258

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of Hunan Province China under Grants 2022JJ30673.

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Central South University, Changsha, 410083, Hunan, China

    Muzhou Hou, Jiaoju Wang, Zhaoying Li, Alphonse Houssou Hounye & Kaifu Wang

  2. Department of Gastroenterology, **angya hospital of Central South University, Changsha, 410008, Hunan, China

    **aowei Liu & Shuijiao Chen

  3. School of Mathematics and Statistics, Nanyang Normal University, Nanyang, 473061, Henan, China

    Jiaoju Wang

  4. School of science, Shaoyang University, Shaoyang, 422099, Hunan, China

    Taohua Liu

Authors
  1. Muzhou Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiaoju Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Taohua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhaoying Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alphonse Houssou Hounye
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. **aowei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kaifu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shuijiao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Kaifu Wang or Shuijiao Chen.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, M., Wang, J., Liu, T. et al. A graph-optimized deep learning framework for recognition of Barrett’s esophagus and reflux esophagitis. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18910-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18910-9

Keywords

Search

Navigation