A NOVEL ATTENTION-BASED U-NET APPROACH FOR RETINAL IMAGE SEGMENTATION ACROSS DIVERSE DATASETS

Omar Abdelhamed; Ahmet Saygılı

doi:10.17780/ksujes.1685741

TR EN

ÇEŞİTLİ VERİ KÜMELERİ ARASINDA RETİNAL GÖRÜNTÜ SEGMENTASYONU İÇİN YENİ BİR DİKKAT TABANLI U-NET YAKLAŞIMI

Öz

Retinal damar segmentasyonu, oftalmik ve sistemik hastalıkların erken tespiti ve tanısı için önemlidir. Bu çalışma, özellikle Att-UNet ve SA-UNet olmak üzere dikkat mekanizmalarının yeni entegrasyonları yoluyla yaygın olarak kullanılan U-Net mimarisinin segmentasyon performansını geliştirmeyi amaçlamaktadır. Bu yöntemleri sistematik olarak değerlendirmek için, üç kıyaslamalı retinal veri kümesinde karşılaştırmalı deneyler yürütülmüştür: DRIVE, STARE ve CHASE-DB1. Modeller, İkili Çapraz Entropi ve Dice kayıp fonksiyonlarını birleştiren bir hibrit kayıp fonksiyonu kullanılarak 80 epok boyunca eğitildi ve performans, Dice Benzerlik Katsayısı (DSC), Birleşim Üzerinden Kesişim (IoU) ve Doğruluk gibi metrikler kullanılarak titizlikle değerlendirildi. Deneysel sonuçlar, dikkatle geliştirilmiş varyantların segmentasyon kalitesi açısından standart U-Net'ten sürekli olarak daha iyi performans gösterdiğini göstermektedir. Özellikle, Att-UNet, DRIVE ve STARE veri kümelerinde en yüksek doğruluğu (%97,37) elde ederek optimum segmentasyon hassasiyeti sağlanırken, SA-UNet en düşük eğitim süreleriyle üstün hesaplama verimliliği göstermiştir. Bu bulgular, dikkat mekanizmalarıyla ilişkili veri kümesine bağlı faydaları ve hesaplamalı takasları vurgulamaktadır. Genel olarak, bu çalışma, dikkat mekanizmaları gibi yeni AI tekniklerinin entegre edilmesinin, retina damar segmentasyonunu önemli ölçüde iyileştirdiğini ve gerçek dünya mühendisliği ve klinik uygulamaları için potansiyel gösterdiğini doğrulamaktadır.

Anahtar Kelimeler

A NOVEL ATTENTION-BASED U-NET APPROACH FOR RETINAL IMAGE SEGMENTATION ACROSS DIVERSE DATASETS

Abstract

Retinal vessel segmentation is essential for the early detection and diagnosis of ophthalmic and systemic diseases. This study aims to enhance the segmentation performance of the widely used U-Net architecture through novel integrations of attention mechanisms, specifically Attention U-Net (Att-UNet) and Spatial Attention U-Net (SA-UNet). To systematically evaluate these methods, comparative experiments were conducted across three benchmark retinal datasets: DRIVE, STARE, and CHASE-DB1. Models were trained for 80 epochs using a hybrid loss combining Binary Cross-Entropy and Dice loss functions, and performance was rigorously assessed using metrics such as Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Accuracy. The experimental results indicate that attention-enhanced variants consistently outperform standard U-Net in terms of segmentation quality. Specifically, Att-UNet achieved the highest accuracy (97.37%) on DRIVE and STARE datasets, providing optimal segmentation precision, whereas SA-UNet demonstrated superior computational efficiency with the lowest training times. These findings highlight the dataset-dependent benefits and computational trade-offs associated with attention mechanisms. Overall, this study confirms that integrating novel AI techniques, such as attention mechanisms, significantly improves retinal vessel segmentation, showcasing potential for real-world engineering and clinical applications.

Keywords

References

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Details

Primary Language

English

Subjects

Image Processing

Journal Section

Research Article

Authors

Omar Abdelhamed
0009-0003-1182-6561
Türkiye

Ahmet Saygılı ^*
0000-0001-8625-4842
Türkiye

Publication Date

September 3, 2025

Submission Date

April 28, 2025

Acceptance Date

July 4, 2025

Published in Issue

Year 2025 Volume: 28 Number: 3

DOI

https://doi.org/10.17780/ksujes.1685741

IZ

https://izlik.org/JA63XN79GG

Cite

RIS / Bibtex

APA

Abdelhamed, O., & Saygılı, A. (2025). A NOVEL ATTENTION-BASED U-NET APPROACH FOR RETINAL IMAGE SEGMENTATION ACROSS DIVERSE DATASETS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1448-1467. https://doi.org/10.17780/ksujes.1685741