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Journals / İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi / 2026 / Cilt: 25 Sayı: 49

COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR ISCHEMIC STROKE SEGMENTATION IN MRI

MRG'DE İSKEMİK İNME SEGMENTASYONU İÇİN DERİN ÖĞRENME MODELLERİNİN KARŞILAŞTIRMALI ANALİZİ

Merve Pınar(MARMARA ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0003-3041-6958
Cemrecan Koç(MARMARA ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0009-0002-4883-6168
Ece Akgül(MARMARA ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0009-0008-9325-0805
Fatih Mert Can Aksoy(MARMARA ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0009-0009-7093-2659
Ayşe Berna Altınel Girgin(MARMARA ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0001-5544-0925
Abdulsamet Aktaş(MARMARA ÜNİVERSİTESİ)0000-0003-0746-7693
Pages
49–76
Publication Date
2026-06-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study aims to bridge the gap between AI-driven lesion detection and real-world clinical applications by determining the advantages and disadvantages of each approach and contributing to more efficient and accurate stroke diagnosis.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Stroke is one of the major global health concerns and a leading cause of death and long-term disability.
📊
Key Findings & Data:This study evaluates the performance of various models for ischemic stroke lesion detection: YOLOv8, YOLO11, and RT-DETR for object detection, and DeepLabv3+ and SegFormer for segmentation.
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Conclusion & Impact:This study aims to bridge the gap between AI-driven lesion detection and real-world clinical applications by determining the advantages and disadvantages of each approach and contributing to more efficient and accurate stroke diagnosis.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:İnme lezyon segmentasyonu · beyin MRI analizi · bilgisayarla görme

Abstract

Stroke is one of the major global health concerns and a leading cause of death and long-term disability. Ischemic stroke occurs when a blockage in cerebral blood flow results in neurological impairment. Early diagnosis and treatment are critical in stroke management, and Magnetic Resonance Imaging (MRI) plays an important role in this phase. However, manual MRI interpretation is highly dependent on expert radiologists and time-consuming, highlighting the need for AI-based lesion detection methods in healthcare. This study evaluates the performance of various models for ischemic stroke lesion detection: YOLOv8, YOLO11, and RT-DETR for object detection, and DeepLabv3+ and SegFormer for segmentation. To enhance data quality and model performance, a comprehensive preprocessing pipeline was implemented. This included 3D to 2D image conversion, intensity normalization, and lesion mask refinement. The models are compared using F1-score and mean precision (mAP) metrics on two public datasets: Anatomical Tracings of Lesions After Stroke (ATLAS_2.0) and Ischemic Stroke Lesion Segmentation (ISLES’22). This study aims to bridge the gap between AI-driven lesion detection and real-world clinical applications by determining the advantages and disadvantages of each approach and contributing to more efficient and accurate stroke diagnosis.

Özet

İnme, küresel çapta önde gelen bir ölüm ve uzun süreli sakatlık nedenidir. Özellikle iskemik inme, beyin kan akışındaki tıkanıklık sonucu nörolojik hasara yol açar. Erken tanı ve tedavi, inme yönetiminde kritiktir ve Manyetik Rezonans Görüntüleme (MRG) bu aşamada önemli bir rol oynamaktadır. Ancak MRG yorumlamasının manuel olması, zaman alıcı ve uzman radyologlara bağımlı bir süreçtir. Bu durum, sağlık hizmetlerindeki yapay zeka tabanlı lezyon saptama yöntemlerine olan ihtiyacı ortaya koymaktadır. Araştırmada, inme lezyonlarının sınırlayıcı kutular ile tespiti için YOLOv8, YOLO11 ve RT-DETR gibi tespit modelleri ile DeepLabv3+ ve SegFormer gibi segmentasyon modellerinin performansını değerlendirilmektedir. Model performansını ve veri kalitesini artırmak amacıyla 3B’den 2B’ye görüntü dönüştürme, yoğunluk normalizasyonu ve lezyon maskesi iyileştirme gibi kapsamlı ön işleme teknikleri kullanılmıştır. Modeller Anatomical Tracings of Lesions After Stroke (ATLAS_2.0), ve Ischemic Stroke Lesion Segmentation (ISLES’22) olmak üzere 2 açık erişimli veri kümesi üzerinde F1-skoru ve ortalama hassasiyet (mAP) metrikleri kullanılarak karşılaştırılmıştır. Bu çalışma, her bir yaklaşımın avantajlarını ve dezavantajlarını belirleyerek, yapay zeka destekli lezyon tespitiyle klinik uygulamalar arasındaki boşluğu kapatmayı ve daha verimli ve doğru inme tanısına katkıda bulunmayı hedeflemektedir.

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Cite This Article

Pınar, M., Koç, C., Akgül, E., Aksoy, F. M. C., Girgin, A. B. A., Aktaş, A., *, A. A. (2026). COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR ISCHEMIC STROKE SEGMENTATION IN MRI. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, Cilt: 25 Sayı: 49, 49–76. https://doi.org/10.55071/ticaretfbd.1745249

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