Gastrointestinal Polyp Segmentation with XAttUNet Based on the Attention-Driven Extended UNet Architecture

Abstract

Timely and accurate detection of gastrointestinal polyps is crucial for the prevention and early diagnosis of colorectal cancer. This study introduces XAttUNet, a novel deep learning architecture developed for the polyp segmentation task. While preserving the basic structure of the UNet architecture, our model integrates attention mechanisms (CBAM), multi-head self-attention layers, and transformer structures to learn both local details and global contextual information more effectively. The proposed model was trained and evaluated on the Kvasir-SEG dataset. Experimental results demonstrate that XAttUNet provides significant improvements in metrics such as Dice, IoU, Precision, Recall, mAP, and Boundary-F1 compared to the classical UNet architecture. Furthermore, visual comparisons demonstrate that the model can capture polyp boundaries with greater precision and realism. In this respect, XAttUNet stands out as a solution that produces highly accurate and reliable results in the field of medical segmentation. This study aims to contribute to the development of deep learning-based polyp detection systems that can be used in clinical support systems.

Keywords

• Segmentation
• Polyp segmentation
• Deep learning
• Attention mechanisms

Dikkat Odaklı Genişletilmiş UNet Mimarisine Dayalı XAttUNet ile Gastrointestinal Polip Segmentasyonu

Abstract

Gastrointestinal poliplerin zamanında ve doğru bir şekilde tespiti, kolorektal kanserin önlenmesi ve erken tanısı açısından büyük önem taşımaktadır. Bu çalışmada, polip segmentasyonu görevine yönelik olarak geliştirilen yeni bir derin öğrenme mimarisi olan XAttUNet tanıtılmaktadır. Modelimiz, UNet mimarisinin temel yapısını korumakla birlikte, dikkat mekanizmalarını (CBAM), çoklu başlı self-attention katmanlarını ve transformer yapılarını entegre ederek, hem lokal detayları hem de küresel bağlamsal bilgiyi daha etkili bir biçimde öğrenebilmektedir. Önerilen model, Kvasir-SEG veri seti üzerinde eğitilmiş ve değerlendirilmiştir. Deneysel sonuçlar, XAttUNet’in klasik UNet mimarisine dayalı modellere kıyasla Dice, IoU, Precision, Recall, mAP ve Boundary-F1 gibi metriklerde anlamlı iyileşmeler sağladığını ortaya koymuştur. Ayrıca görsel karşılaştırmalar, modelin polip sınırlarını daha hassas ve gerçekçi şekilde yakalayabildiğini göstermektedir. Bu yönüyle XAttUNet, tıbbi segmentasyon alanında doğruluğu yüksek ve güvenilir sonuçlar üreten bir çözüm olarak öne çıkmaktadır. Yapılan bu çalışma, klinik destek sistemlerinde kullanılabilecek derin öğrenme temelli polip tespiti sistemlerinin geliştirilmesine katkı sunmayı amaçlamaktadır.

Keywords

• Segmentasyon
• Polip segmentasyonu
• Derin öğrenme
• Dikkat mekanizmaları
• Transformer

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