ColoSegNet: Visual Intelligence Driven Triple Attention Feature Fusion Network for Endoscopic Colorectal Cancer Segmentation

Talha Salman; Alexandros Gazis; Aizaz Ali; Muhammad Hassan Khan; Muhammad Ali; Haseeb Khan; Hasnain Ali Shah

doi:10.62762/TIS.2025.385365

Article Information

Published in ICCK Transactions on Intelligent Systematics

Volume/Issue Volume 2, Issue 2, 2025

Pages 125-136

Cited by 4 (Crossref) 4 (Scopus)

Abstract

Accurate segmentation of colorectal cancer (CRC) from endoscopic images is crucial for computer-aided diagnosis. Visual intelligence enhances detection precision, supporting clinical decision-making. However, current segmentation methods often struggle with accurately delineating fine-grained lesion boundaries due to limited context comprehension and inadequate attention to optimal features. Additionally, the poor fusion of multi-scale semantic cues hinders performance, especially in complex endoscopic scenarios. To address these issues, we introduce ColoSegNet, a Visual Intelligence-Driven Triple Attention Feature Fusion Network designed for high-precision CRC segmentation. Our approach begins with an analysis of backbone networks to identify optimal intermediate-level feature extraction. The architecture includes a non-local attention module to capture long-range dependencies, enhanced by channel and spatial attention mechanisms for better feature selectivity across semantic hierarchies. Multi-scale fusion strategies are used to preserve both low-level details and high-level context, enabling accurate localization under challenging conditions. Extensive evaluations across four publicly available endoscopic datasets, along with cross-dataset assessments, demonstrate the effectiveness and generalizability of ColoSegNet, outperforming state-of-the-art segmentation methods.

Graphical Abstract

ColoSegNet: Visual Intelligence Driven Triple Attention Feature Fusion Network for Endoscopic Colorectal Cancer Segmentation

Keywords

colorectal segmentation visual intelligence medical imaging attention mechanisms feature fusion semantic segmentation deep learning

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

This study utilized only publicly available datasets (KvasirSEG, ClinicDB, ColonDB, Endoscene, ETIS) with pre-existing ethical approvals. No additional human or animal participation was involved, hence no further ethical approval was required.

References

World Cancer Research Fund International. (2022). Colorectal Cancer Statistics. Retrieved from https://www.wcrf.org/preventing-cancer/cancer-statistics/colorectal-cancer-statistics/
[Google Scholar]
Fan, D. P., Ji, G. P., Zhou, T., Chen, G., Fu, H., Shen, J., & Shao, L. (2020, September). Pranet: Parallel reverse attention network for polyp segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 263-273). Cham: Springer International Publishing.
[CrossRef] [Google Scholar]
Dong, B., Wang, W., Fan, D., Li, J., Fu, H., & Shao, L. (2023). Polyp-PVT: Polyp segmentation with pyramid vision transformers. CAAI Artificial Intelligence Research, 9150015.
[CrossRef] [Google Scholar]
Kim, T., Lee, H., & Kim, D. (2021, October). Uacanet: Uncertainty augmented context attention for polyp segmentation. In Proceedings of the 29th ACM international conference on multimedia (pp. 2167-2175).
[CrossRef] [Google Scholar]
Rahman, M. M., & Marculescu, R. (2023). Medical image segmentation via cascaded attention decoding. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 6222-6231).
[CrossRef] [Google Scholar]
Cao, X., Yu, H., Yan, K., Cui, R., Guo, J., Li, X., ... & Huang, T. (2024). DEMF-Net: A dual encoder multi-scale feature fusion network for polyp segmentation. Biomedical Signal Processing and Control, 96, 106487.
[CrossRef] [Google Scholar]
Wu, H., Zhao, Z., & Wang, Z. (2023). META-Unet: Multi-scale efficient transformer attention Unet for fast and high-accuracy polyp segmentation. IEEE Transactions on Automation Science and Engineering, 21(3), 4117-4128.
[CrossRef] [Google Scholar]
Li, J., Wang, J., Lin, F., Heidari, A. A., Chen, Y., Chen, H., & Wu, W. (2024). PRCNet: A parallel reverse convolutional attention network for colorectal polyp segmentation. Biomedical Signal Processing and Control, 95, 106336.
[CrossRef] [Google Scholar]
Jiang, Y., Xu, S., Fan, H., Qian, J., Luo, W., Zhen, S., ... & Lin, H. (2021). Ala-net: Adaptive lesion-aware attention network for 3d colorectal tumor segmentation. IEEE transactions on medical imaging, 40(12), 3627-3640.
[CrossRef] [Google Scholar]
Pei, Y., Mu, L., Fu, Y., He, K., Li, H., Guo, S., ... & Li, X. (2020). Colorectal tumor segmentation of CT scans based on a convolutional neural network with an attention mechanism. IEEE Access, 8, 64131-64138.
[CrossRef] [Google Scholar]
Santhoshi, A., & Muthukumaravel, A. (2024). Enhancing Colorectal Cancer Diagnosis With Machine Learning Algorithms. In Advancing Intelligent Networks Through Distributed Optimization (pp. 165-184). IGI Global.
[CrossRef] [Google Scholar]
Sharma, S., Verma, S., Chaudhuri, S., & Saxena, A. (2024, April). Sustainable Development in Cancer Diagnosis: Empowering Precision Medicine with Artificial Intelligence and CRC Detection Tools. In International Conference on Sustainable Development through Machine Learning, AI and IoT (pp. 81-91). Cham: Springer Nature Switzerland.
[CrossRef] [Google Scholar]
Patharia, P., Sethy, P. K., & Nanthaamornphong, A. (2024). Advancements and Challenges in the Image-Based Diagnosis of Lung and Colon Cancer: A Comprehensive Review. Cancer Informatics, 23, 11769351241290608.
[CrossRef] [Google Scholar]
Fang, Y., Chen, C., Yuan, Y., & Tong, K. Y. (2019). Selective feature aggregation network with area-boundary constraints for polyp segmentation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I 22 (pp. 302-310). Springer International Publishing.
[CrossRef] [Google Scholar]
Hatamizadeh, A., Terzopoulos, D., & Myronenko, A. (2019, October). End-to-end boundary aware networks for medical image segmentation. In International Workshop on Machine Learning in Medical Imaging (pp. 187-194). Cham: Springer International Publishing.
[CrossRef] [Google Scholar]
Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., De Lange, T., Halvorsen, P., & Johansen, H. D. (2019, December). Resunet++: An advanced architecture for medical image segmentation. In 2019 IEEE international symposium on multimedia (ISM) (pp. 225-2255). IEEE.
[CrossRef] [Google Scholar]
Murugesan, B., Sarveswaran, K., Shankaranarayana, S. M., Ram, K., Joseph, J., & Sivaprakasam, M. (2019, July). Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation. In 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 7223-7226). IEEE.
[CrossRef] [Google Scholar]
Hu, K., Chen, W., Sun, Y., Hu, X., Zhou, Q., & Zheng, Z. (2023). PPNet: Pyramid pooling based network for polyp segmentation. Computers in biology and medicine, 160, 107028.
[CrossRef] [Google Scholar]
He, J., Deng, Z., & Qiao, Y. (2019). Dynamic multi-scale filters for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3562-3572).
[CrossRef] [Google Scholar]
Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th international workshop, DLMIA 2018, and 8th international workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, proceedings 4 (pp. 3-11). Springer International Publishing.
[CrossRef] [Google Scholar]
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., ... & Zhou, Y. (2021). TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.
[Google Scholar]
Lin, L., Lv, G., Wang, B., Xu, C., & Liu, J. (2024). Polyp-LVT: Polyp segmentation with lightweight vision transformers. Knowledge-Based Systems, 300, 112181.
[CrossRef] [Google Scholar]
Valanarasu, J. M. J., Oza, P., Hacihaliloglu, I., & Patel, V. M. (2021). Medical transformer: Gated axial-attention for medical image segmentation. In Medical image computing and computer assisted intervention–MICCAI 2021: 24th international conference, Strasbourg, France, September 27–October 1, 2021, proceedings, part I 24 (pp. 36-46). Springer International Publishing.
[CrossRef] [Google Scholar]
Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S. K., & Cui, S. (2021). Shallow attention network for polyp segmentation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24 (pp. 699-708). Springer International Publishing.
[CrossRef] [Google Scholar]
Yue, G., Zhuo, G., Yan, W., Zhou, T., Tang, C., Yang, P., & Wang, T. (2024). Boundary uncertainty aware network for automated polyp segmentation. Neural Networks, 170, 390-404.
[CrossRef] [Google Scholar]
Sinha, A., & Dolz, J. (2020). Multi-scale self-guided attention for medical image segmentation. IEEE journal of biomedical and health informatics, 25(1), 121-130.
[CrossRef] [Google Scholar]
Ahamed, M. F., Islam, M. R., Nahiduzzaman, M., Chowdhury, M. E., Alqahtani, A., & Murugappan, M. (2024). Automated colorectal polyps detection from endoscopic images using MultiResUNet framework with attention guided segmentation. Human-Centric Intelligent Systems, 4(2), 299-315.
[CrossRef] [Google Scholar]
Liu, D., Lu, C., Sun, H., & Gao, S. (2024). NA-segformer: A multi-level transformer model based on neighborhood attention for colonoscopic polyp segmentation. Scientific Reports, 14(1), 22527.
[CrossRef] [Google Scholar]
Liu, J., Chen, Q., Zhang, Y., Wang, Z., Deng, X., & Wang, J. (2024). Multi-level feature fusion network combining attention mechanisms for polyp segmentation. Information Fusion, 104, 102195.
[CrossRef] [Google Scholar]
Cai, L., Wu, M., Chen, L., Bai, W., Yang, M., Lyu, S., & Zhao, Q. (2022, September). Using guided self-attention with local information for polyp segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 629-638). Cham: Springer Nature Switzerland.
[CrossRef] [Google Scholar]
Yang, H., Chen, Q., Fu, K., Zhu, L., Jin, L., Qiu, B., ... & Lu, Y. (2022). Boosting medical image segmentation via conditional-synergistic convolution and lesion decoupling. Computerized Medical Imaging and Graphics, 101, 102110.
[CrossRef] [Google Scholar]
Jeong, S. M., Lee, S. G., Seok, C. L., Lee, E. C., & Lee, J. Y. (2023). Lightweight deep learning model for real-time colorectal polyp segmentation. Electronics, 12(9), 1962.
[CrossRef] [Google Scholar]
Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015). WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized medical imaging and graphics, 43, 99-111.
[CrossRef] [Google Scholar]
Tajbakhsh, N., Gurudu, S. R., & Liang, J. (2015). Automated polyp detection in colonoscopy videos using shape and context information. IEEE transactions on medical imaging, 35(2), 630-644.
[CrossRef] [Google Scholar]
Vázquez, D., Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., López, A. M., Romero, A., ... & Courville, A. (2017). A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of healthcare engineering, 2017(1), 4037190.
[CrossRef] [Google Scholar]
Silva, J., Histace, A., Romain, O., Dray, X., & Granado, B. (2014). Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. International journal of computer assisted radiology and surgery, 9, 283-293.
[CrossRef] [Google Scholar]
Zhang, R., Li, G., Li, Z., Cui, S., Qian, D., & Yu, Y. (2020). Adaptive context selection for polyp segmentation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI 23 (pp. 253-262). Springer International Publishing.
[CrossRef] [Google Scholar]
Qiu, Z., Wang, Z., Zhang, M., Xu, Z., Fan, J., & Xu, L. (2022, April). BDG-Net: boundary distribution guided network for accurate polyp segmentation. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 792-799). SPIE.
[CrossRef] [Google Scholar]
Wang, J., Huang, Q., Tang, F., Meng, J., Su, J., & Song, S. (2022, September). Stepwise feature fusion: Local guides global. In International conference on medical image computing and computer-assisted intervention (pp. 110-120). Cham: Springer Nature Switzerland.
[CrossRef] [Google Scholar]

Cited By (4)

Časlav Livada, Tomislav Galba, Alfonzo Baumgartner, Krešimir Nenadić. A Hybrid Chaotic Bat Algorithm With Deep Learning Optimization for Multi-Level Image Segmentation. IEEE Access, 2026 , 14 .
[CrossRef]
Chengpeng Tian, Yuanyuan Peng, Yan Xi, Bin Tang, Qingkai Ma, Na Cheng, Zhen Liang, Kai Xu. A parallel hybrid convolutional state space network with multi-attention mechanisms for retinal vessel segmentation. Biomedical Signal Processing and Control, 2026 , 116 .
[CrossRef]
Yiming Xiao, Chunxi Yang, Xian Wang, Faxiang Zhang, Long Wu. . 2026 IEEE 15th Data Driven Control and Learning Systems (DDCLS), 2026 .
[CrossRef]
Elena Sibilano, Claudia Delprete, Pietro Maria Marvulli, Antonio Brunetti, Francescomaria Marino, Giuseppe Lucarelli, Michele Battaglia, Vitoantonio Bevilacqua. Deep Learning Strategies for Semantic Segmentation in Robot-Assisted Radical Prostatectomy. Applied Sciences, 2025 , 15 (19).
[CrossRef]

* Citation data provided by Crossref Cited-by.

Cite This Article

APA Style

Salman, T., Gazis, A., Ali, A. Khan, M. H., Ali, M., Khan, H., & Shah, H. A. (2025). ColoSegNet: Visual Intelligence Driven Triple Attention Feature Fusion Network for Endoscopic Colorectal Cancer Segmentation. ICCK Transactions on Intelligent Systematics, 2(2), 125–136. https://doi.org/10.62762/TIS.2025.385365

Export Citation

RIS Format

Compatible with EndNote, Zotero, Mendeley, and other reference managers

TY  - JOUR
AU  - Salman, Talha
AU  - Gazis, Alexandros
AU  - Ali, Aizaz
AU  - Khan, Muhammad Hassan
AU  - Ali, Muhammad
AU  - Khan, Haseeb
AU  - Shah, Hasnain Ali
PY  - 2025
DA  - 2025/06/25
TI  - ColoSegNet: Visual Intelligence Driven Triple Attention Feature Fusion Network for Endoscopic Colorectal Cancer Segmentation
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 2
IS  - 2
SP  - 125
EP  - 136
DO  - 10.62762/TIS.2025.385365
UR  - https://www.icck.org/article/abs/TIS.2025.385365
KW  - colorectal segmentation
KW  - visual intelligence
KW  - medical imaging
KW  - attention mechanisms
KW  - feature fusion
KW  - semantic segmentation
KW  - deep learning
AB  - Accurate segmentation of colorectal cancer (CRC) from endoscopic images is crucial for computer-aided diagnosis. Visual intelligence enhances detection precision, supporting clinical decision-making. However, current segmentation methods often struggle with accurately delineating fine-grained lesion boundaries due to limited context comprehension and inadequate attention to optimal features. Additionally, the poor fusion of multi-scale semantic cues hinders performance, especially in complex endoscopic scenarios. To address these issues, we introduce ColoSegNet, a Visual Intelligence-Driven Triple Attention Feature Fusion Network designed for high-precision CRC segmentation. Our approach begins with an analysis of backbone networks to identify optimal intermediate-level feature extraction. The architecture includes a non-local attention module to capture long-range dependencies, enhanced by channel and spatial attention mechanisms for better feature selectivity across semantic hierarchies. Multi-scale fusion strategies are used to preserve both low-level details and high-level context, enabling accurate localization under challenging conditions. Extensive evaluations across four publicly available endoscopic datasets, along with cross-dataset assessments, demonstrate the effectiveness and generalizability of ColoSegNet, outperforming state-of-the-art segmentation methods.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

BibTeX Format

Compatible with LaTeX, BibTeX, and other reference managers

@article{Salman2025ColoSegNet,
  author = {Talha Salman and Alexandros Gazis and Aizaz Ali and Muhammad Hassan Khan and Muhammad Ali and Haseeb Khan and Hasnain Ali Shah},
  title = {ColoSegNet: Visual Intelligence Driven Triple Attention Feature Fusion Network for Endoscopic Colorectal Cancer Segmentation},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2025},
  volume = {2},
  number = {2},
  pages = {125-136},
  doi = {10.62762/TIS.2025.385365},
  url = {https://www.icck.org/article/abs/TIS.2025.385365},
  abstract = {Accurate segmentation of colorectal cancer (CRC) from endoscopic images is crucial for computer-aided diagnosis. Visual intelligence enhances detection precision, supporting clinical decision-making. However, current segmentation methods often struggle with accurately delineating fine-grained lesion boundaries due to limited context comprehension and inadequate attention to optimal features. Additionally, the poor fusion of multi-scale semantic cues hinders performance, especially in complex endoscopic scenarios. To address these issues, we introduce ColoSegNet, a Visual Intelligence-Driven Triple Attention Feature Fusion Network designed for high-precision CRC segmentation. Our approach begins with an analysis of backbone networks to identify optimal intermediate-level feature extraction. The architecture includes a non-local attention module to capture long-range dependencies, enhanced by channel and spatial attention mechanisms for better feature selectivity across semantic hierarchies. Multi-scale fusion strategies are used to preserve both low-level details and high-level context, enabling accurate localization under challenging conditions. Extensive evaluations across four publicly available endoscopic datasets, along with cross-dataset assessments, demonstrate the effectiveness and generalizability of ColoSegNet, outperforming state-of-the-art segmentation methods.},
  keywords = {colorectal segmentation, visual intelligence, medical imaging, attention mechanisms, feature fusion, semantic segmentation, deep learning},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}

Article Metrics

Citations

Crossref

4

Scopus

4

Views

3161

PDF Downloads

476

Publisher's Note

ICCK stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and Permissions

Institute of Central Computation and Knowledge (ICCK) or its licensor 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.

ICCK Transactions on Intelligent Systematics

ISSN: 3068-5079 (Online) | ISSN: 3069-003X (Print)

[email protected]

Preserved at
Portico

User

Unlimited Downloads

Complete Library Access

Membership Eligibility

Community Leadership Opportunities