TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps with Ambiguous Boundaries

Burhan Baraklı; Ahmet Küçüker

doi:10.35377/saucis...1798069

EN

TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps with Ambiguous Boundaries

Abstract

Colorectal cancer (CRC) is one of the most common and deadly types of cancer worldwide. During standard colonoscopy procedures to detect polyps, which are early-stage precancerous lesions critical for disease prevention, challenges exist, such as overlooking polyps and the inability to accurately segment polyps with weak borders that are integrated with surrounding tissue using current computer-aided methods. This study proposes a new deep learning architecture, called TriaNet (Tri-Fusion Attention Network), to enhance the segmentation accuracy of polyps with weak borders. The fundamental innovation of TriaNet is its unique “triple-fusion” attention mechanism, which combines three complementary information streams. The proposed method dynamically fuses edge feature information obtained from a hybrid block containing Scharr, DoG, and Gabor filters, the semantic feature map from the decoder structure, and an instantaneous boundary map derived from a Scharr operator applied to an upper layer prediction. Furthermore, Deformable Alignment layers are utilized in skip connections to enhance the model's ability to adapt to variable polyp morphologies. The TriaNET architecture was tested on four different benchmark datasets, including Kvasir-SEG, CVC-ColonDB, ETIS-LaribPolypDB, and CVC-300, which demonstrated superior performance compared to state-of-the-art methods.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Burhan Baraklı ^*
0000-0002-7947-2312
Türkiye

Ahmet Küçüker
0000-0001-9412-5223
Türkiye

Early Pub Date

December 11, 2025

Publication Date

December 29, 2025

Submission Date

October 6, 2025

Acceptance Date

November 3, 2025

Published in Issue

Year 2025 Volume: 8 Number: 4

DOI

https://doi.org/10.35377/saucis...1798069

IZ

https://izlik.org/JA89HE36UJ

Cite

RIS / Bibtex

APA

Baraklı, B., & Küçüker, A. (2025). TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps with Ambiguous Boundaries. Sakarya University Journal of Computer and Information Sciences, 8(4), 798-811. https://doi.org/10.35377/saucis...1798069

AMA

1.Baraklı B, Küçüker A. TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps with Ambiguous Boundaries. SAUCIS. 2025;8(4):798-811. doi:10.35377/saucis.1798069

Chicago

Baraklı, Burhan, and Ahmet Küçüker. 2025. “TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps With Ambiguous Boundaries”. Sakarya University Journal of Computer and Information Sciences 8 (4): 798-811. https://doi.org/10.35377/saucis. 1798069.

EndNote

Baraklı B, Küçüker A (December 1, 2025) TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps with Ambiguous Boundaries. Sakarya University Journal of Computer and Information Sciences 8 4 798–811.

IEEE

[1]B. Baraklı and A. Küçüker, “TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps with Ambiguous Boundaries”, SAUCIS, vol. 8, no. 4, pp. 798–811, Dec. 2025, doi: 10.35377/saucis...1798069.

ISNAD

Baraklı, Burhan - Küçüker, Ahmet. “TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps With Ambiguous Boundaries”. Sakarya University Journal of Computer and Information Sciences 8/4 (December 1, 2025): 798-811. https://doi.org/10.35377/saucis. 1798069.

JAMA

1.Baraklı B, Küçüker A. TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps with Ambiguous Boundaries. SAUCIS. 2025;8:798–811.

MLA

Baraklı, Burhan, and Ahmet Küçüker. “TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps With Ambiguous Boundaries”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 4, Dec. 2025, pp. 798-11, doi:10.35377/saucis. 1798069.

Vancouver

1.Burhan Baraklı, Ahmet Küçüker. TriaNet: A Tri-Fusion Attention Network for Segmenting Polyps with Ambiguous Boundaries. SAUCIS. 2025 Dec. 1;8(4):798-811. doi:10.35377/saucis. 1798069