Skip to main content
Springer Nature Link
Log in

Small gastric polyp detection based on the improved YOLOv5

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

Abstract

Small target polyps are prone to missed detection due to their small coverage area and little information. To address this issue, a modified PATM-YOLO polyp detection model based on YOLOv5 is proposed. The model first addresses the issue of missed detection of small polyps by constructing a detection head for identifying small polyps and using an improved Phase-Aware Token Mixing Module(PATM) attention module to increase the network’s attention to small polyps and suppress the model’s focus on non-polyp regions. Secondly, an improved Adaptively Spatial Feature Fusion(ASFF) module is proposed to fully utilize multi-scale information, enhancing the network’s feature richness. Finally, by introducing the Swin Transformer into the network and determining its optimal placement through experiments, the detection accuracy is maximized without affecting the network’s performance. After experimental comparison on the constructed dataset and the public dataset SUN, the proposed PATM-YOLO network model alleviated missed detection in dense and small polyp images, and achieved an precision of 91.3\(\%\), which is 8.5\(\%\) higher than the baseline YOLOv5 network model. This indicates that the detection performance of this model outperforms other classical target detection networks and the original network.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Code Availibility

Not applicable

Availability of data and materials

Three publicly available polyp datasets of endoscopy images (the PLoS One-Zhang dataset, the Hyper-Kvasir-Segmentation dataset, and the SUN dataset) were used in the experiments of this study. The PLoS One-Zhang dataset can be found at: https://github.com/jiquan/Dataset-acess-for-PLOS-ONE. The Hyper-Kvasir-Segmentation dataset can be found at: https://datasets.simula.no/hyper-kvasir/. The SUN dataset can be found at: http://sundatabase.org.

References

  1. Brosens LA, Wood LD, Offerhaus GJ, Arnold CA, Lam-Himlin D, Giardiello FM, Montgomery EA (2016) Pathology and genetics of syndromic gastric polyps. International journal of surgical pathology 24(3):185–199. https://doi.org/10.1177/1066896915620013

    Article  Google Scholar 

  2. Ameling S, Wirth S, Paulus D, Lacey G, Vilarino F (2009) Texture-based polyp detection in colonoscopy. In: Bildverarbeitung Für die Medizin 2009: Algorithmen—Systeme—Anwendungen Proceedings des Workshops Vom 22. Bis 25. März 2009 in Heidelberg, Springer, pp 346–350. https://doi.org/10.1007/978-3-540-93860-6_70

  3. Iakovidis DK, Maroulis DE, Karkanis SA, Brokos A (2005) A comparative study of texture features for the discrimination of gastric polyps in endoscopic video. In: 18th IEEE Symposium on computer-based medical systems (CBMS’05), IEEE, pp 575–580. https://doi.org/10.1109/CBMS.2005.6

  4. Hwang S, Oh J, Tavanapong W, Wong J, De Groen PC (2007) Polyp detection in colonoscopy video using elliptical shape feature. In: 2007 IEEE International conference on image processing, IEEE, 2:465. https://doi.org/10.1109/ICIP.2007.4379193

  5. Bernal J, Sánchez J, Vilarino F (2012) Towards automatic polyp detection with a polyp appearance model. Pattern Recognition 45(9):3166–3182. https://doi.org/10.1016/j.patcog.2012.03.002

    Article  Google Scholar 

  6. Leung WK, Guo C-G, Ko MK, To EW, Mak LY, Tong TS, Chen L-J, But DY, Wong SY, Liu KS et al (2020) Linked color imaging versus narrow-band imaging for colorectal polyp detection: a prospective randomized tandem colonoscopy study. Gastrointestinal Endoscopy 91(1):104–112. https://doi.org/10.1016/j.gie.2019.06.031

    Article  Google Scholar 

  7. Alexandre LA, Nobre N, Casteleiro J (2008) Color and position versus texture features for endoscopic polyp detection. In: 2008 International conference on biomedical engineering and informatics, IEEE, 2:38–42. https://doi.org/10.1109/BMEI.2008.246

  8. Freedman JS, Harari DY, Bamji ND, Bodian CA, Kornacki S, Cohen LB, Miller KM, Aisenberg J (2011) The detection of premalignant colon polyps during colonoscopy is stable throughout the workday. Gastrointestinal endoscopy 73(6):1197–1206. https://doi.org/10.1016/j.gie.2011.01.019

    Article  Google Scholar 

  9. Simmons DT, Harewood GC, Baron TH, Petersen BT, Wang KK, Boyd-Enders F, Ott BJ (2006) Impact of endoscopist withdrawal speed on polyp yield: implications for optimal colonoscopy withdrawal time. Alimentary pharmacology & therapeutics 24(6):965–971. https://doi.org/10.1016/j.gie.2006.03.026

    Article  Google Scholar 

  10. Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, Van Riel SJ, Wille MMW, Naqibullah M, Sánchez CI, Van Ginneken B (2016) Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks. IEEE transactions on medical imaging 35(5):1160–1169. https://doi.org/10.1109/TMI.2016.2536809

    Article  Google Scholar 

  11. Pang S, Ding T, Qiao S, Meng F, Wang S, Li P, Wang X (2019) A novel yolov3-arch model for identifying cholelithiasis and classifying gallstones on ct images. PloS one 14(6):0217647. https://doi.org/10.1371/journal.pone.0217647

    Article  Google Scholar 

  12. Deeba F, Bui FM, Wahid KA (2020) Computer-aided polyp detection based on image enhancement and saliency-based selection. Biomed Signal Process Control 55:101530. https://doi.org/10.1016/j.bspc.2019.04.007

  13. Qadir HA, Shin Y, Solhusvik J, Bergsland J, Aabakken L, Balasingham I (2021) Toward real-time polyp detection using fully cnns for 2d gaussian shapes prediction. Med Image Anal 68:101897. https://doi.org/10.1016/j.media.2020.101897

  14. Taş M, Yılmaz B (2021) Super resolution convolutional neural network based pre-processing for automatic polyp detection in colonoscopy images. Comput Electrical Eng 90:106959. https://doi.org/10.1016/j.compeleceng.2020.106959

  15. Chen B-L, Wan J-J, Chen T-Y, Yu Y-T, Ji M (2021) A self-attention based faster r-cnn for polyp detection from colonoscopy images. Biomed Signal Process Control 70:103019. https://doi.org/10.1016/j.bspc.2021.103019

  16. Cao C, Wang R, Yu Y, Zhang H, Yu Y, Sun C (2021) Gastric polyp detection in gastroscopic images using deep neural network. PloS one 16(4):0250632. https://doi.org/10.1371/journal.pone.0250632

    Article  Google Scholar 

  17. Nisha J, Gopi VP, Palanisamy P (2022) Automated colorectal polyp detection based on image enhancement and dual-path cnn architecture. Biomed Signal Process Control 73:103465. https://doi.org/10.1016/j.bspc.2021.103465

  18. Hu K, Zhao L, Feng S, Zhang S, Zhou Q, Gao X, Guo Y (2022) Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement. Comput Biol Med 147:105760. https://doi.org/10.1016/j.compbiomed.2022.105760

  19. Zhu X, Lyu S, Wang X, Zhao Q (2021) Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 2778–2788. https://doi.org/10.48550/arXiv.2108.11539

  20. Tang Y, Han K, Guo J, Xu C, Li Y, Xu C, Wang Y (2022) An image patch is a wave: Phase-aware vision mlp. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp 10935–10944. https://doi.org/10.48550/arXiv.2111.12294

  21. Liu Z, Hu H, Lin Y, Yao Z, Xie Z, Wei Y, Ning J, Cao Y, Zhang Z, Dong L, et al. (2022) Swin transformer v2: Scaling up capacity and resolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12009–12019. https://doi.org/10.48550/arXiv.2111.09883

  22. Liu S, Huang D, Wang Y (2019) Learning spatial fusion for single-shot object detection. arXiv preprint arXiv:1911.09516. https://doi.org/10.48550/arXiv.1911.09516

  23. Zhang X, Chen F, Yu T, An J, Huang Z, Liu J, Hu W, Wang L, Duan H, Si J (2019) Real-time gastric polyp detection using convolutional neural networks. PloS one 14(3):0214133. https://doi.org/10.1371/journal.pone.0214133

    Article  Google Scholar 

  24. 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):283. https://doi.org/10.1038/s41597-020-00622-y

    Article  Google Scholar 

  25. Misawa M, Kudo S-E, Mori Y, Hotta K, Ohtsuka K, Matsuda T, Saito S, Kudo T, Baba T, Ishida F et al (2021) Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video). Gastrointestinal endoscopy 93(4):960–967. https://doi.org/10.1016/j.gie.2020.07.060

    Article  Google Scholar 

  26. Pacal I, Karaman A, Karaboga D, Akay B, Basturk A, Nalbantoglu U, Coskun S (2022) An efficient real-time colonic polyp detection with yolo algorithms trained by using negative samples and large datasets. Comput Biol Med 141:105031. https://doi.org/10.1016/j.compbiomed.2021.105031

  27. Karaman A, Pacal I, Basturk A, Akay B, Nalbantoglu U, Coskun S, Sahin O, Karaboga D (2023) Robust real-time polyp detection system design based on yolo algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (abc). Expert Syst Appl 221:119741. https://doi.org/10.1016/j.eswa.2023.119741

Download references

Funding

This work was supported by the Shanghai Science and Technology Innovation Action Plan(22S31903700 \( \& \) 21S31904200).

Author information

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333 Longteng Road, Shanghai, 201620, People’s Republic of China

    Linfei Wu, Jin Liu, Bo Huang, Haishan Liu & Shaowei Cheng

  2. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai, 200093, People’s Republic of China

    Haima Yang

Authors
  1. Linfei Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jin Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Haima Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Bo Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Haishan Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Shaowei Cheng
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

LW, JL, and HY contributed to conception and design of the study. LW, HY, HL, and SC organized the database. LW, JL and SC performed the statistical analysis. LW wrote the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Corresponding author

Correspondence to Jin Liu.

Ethics declarations

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for publication

Not applicable

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

Wu, L., Liu, J., Yang, H. et al. Small gastric polyp detection based on the improved YOLOv5. Multimed Tools Appl 83, 71773–71788 (2024). https://doi.org/10.1007/s11042-024-18497-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-024-18497-1

Keywords