A NEW O-NET MODEL FOR AUTOMATED BRAIN TUMOR SEGMENTATION ON MAGNETIC RESONANCE IMAGES

Süleyman Uzun; Ekin Ekinci; Şevket Ay

doi:10.17780/ksujes.1674766

Research Article

MANYETİK REZONANS GÖRÜNTÜLERİNDE OTOMATIK BEYİN TÜMÖRÜ SEGMENTASYONU İÇİN YENİ BİR O-NET MODELİ

Year 2025, Volume: 28 Issue: 3, 1362 - 1374, 03.09.2025

Süleyman Uzun , Ekin Ekinci , Şevket Ay

https://doi.org/10.17780/ksujes.1674766

Abstract

Kanser, küresel ölüm oranlarına önemli ölçüde katkıda bulunan bir hastalık olup, beyin tümörleri en tehlikeli kanser türleri arasında yer almaktadır. Erken teşhis ve uygun tedavi, hastaların yaşam süresi ve yaşam kalitesi üzerinde büyük etkiye sahiptir. Ayrıca, tümör tipinin doğru belirlenmesi, tedavi planlaması ve kişiselleştirilmiş tedavi stratejilerinin oluşturulması açısından kritik öneme sahiptir. Bu nedenle, beyin tümörlerinin türünü belirlemek için güvenilir ve etkili bir görüntüleme ve analiz yöntemine ihtiyaç vardır. Son yıllarda, derin öğrenme yöntemlerindeki ilerlemeler tıbbi görüntüleme alanında önemli gelişmelere yol açmıştır. Bu çalışmada, beyin tümörlerinin segmentasyonunu otomatikleştirmek için özel olarak tasarlanan O-Net adlı derin öğrenme modeli tanıtılmaktadır. U-Net mimarisini temel alan O-Net, gelişmiş yapısı ve derinliği sayesinde üstün performans sergilemektedir. Beyin Manyetik Rezonans Görüntüleri (MRI) kullanılarak yapılan deneysel değerlendirmelerde, O-Net modelinin literatürdeki diğer çalışmalara kıyasla daha yüksek performans gösterdiği belirlenmiştir. Model, %90 Dice skoru ile yüksek doğruluk oranına ulaşmıştır. Elde edilen sonuçlar, O-Net modelinin beyin tümörlerinin doğru tespiti ve segmentasyonu için güvenilir bir araç olduğunu göstermektedir. O-Net’in gelişmiş versiyonu, gelecekte klinik uygulamalarda beyin tümörlerinin tanı ve tedavisinde etkili bir araç olarak kullanılabilir.

Keywords

Beyin tümörü , medikal görüntüleme , otomatik segmentasyon , derin öğrenme , O-Net

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A NEW O-NET MODEL FOR AUTOMATED BRAIN TUMOR SEGMENTATION ON MAGNETIC RESONANCE IMAGES

Year 2025, Volume: 28 Issue: 3, 1362 - 1374, 03.09.2025

Süleyman Uzun , Ekin Ekinci , Şevket Ay

https://doi.org/10.17780/ksujes.1674766

Abstract

Cancer is a major contributor to global mortality rates, with brain tumors being among the most dangerous types. Early diagnosis and appropriate treatment significantly impact patients' survival rates and quality of life. Moreover, accurately determining the tumor type is crucial for treatment planning and the development of personalized treatment strategies. Therefore, a reliable and effective imaging and analysis method is required to classify brain tumors accurately. In recent years, advancements in deep learning techniques have led to significant progress in medical imaging. This study introduces O-Net, a deep learning model specifically designed to automate brain tumor segmentation. Based on the U-Net architecture, O-Net exhibits a sophisticated structure and depth, enabling superior performance. Experimental evaluations using brain Magnetic Resonance Imaging (MRI) revealed that the O-Net model outperformed other studies in the literature. The model achieved a high accuracy rate with a 90% Dice score. These results demonstrate that O-Net is a reliable tool for the accurate detection and segmentation of brain tumors. The advanced version of the O-Net model can be effectively utilized in future clinical applications for the diagnosis and treatment of brain tumors.

Keywords

Brain tumor , medical imaging , automatic segmentation , deep learning , O-Net

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Primary Language	English
Subjects	Image Processing
Journal Section	Computer Engineering
Authors	Süleyman Uzun 0000-0001-8246-6733 Ekin Ekinci 0000-0003-0658-592X Şevket Ay 0000-0001-8422-4615
Publication Date	September 3, 2025
Submission Date	April 12, 2025
Acceptance Date	May 21, 2025
Published in Issue	Year 2025 Volume: 28 Issue: 3

Cite

APA	Uzun, S., Ekinci, E., & Ay, Ş. (2025). A NEW O-NET MODEL FOR AUTOMATED BRAIN TUMOR SEGMENTATION ON MAGNETIC RESONANCE IMAGES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1362-1374. https://doi.org/10.17780/ksujes.1674766

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