U-Net-Based Models for Precise Brain Stroke Segmentation

Suat İnce; Ismail Kunduracioglu; Bilal Bayram; Ishak Pacal

doi:10.51537/chaos.1605529

Research Article

Year 2025, Volume: 7 Issue: 1, 50 - 60, 31.03.2025

Suat İnce , Ismail Kunduracioglu , Bilal Bayram , Ishak Pacal

https://doi.org/10.51537/chaos.1605529

Cited By: 26

Abstract

References

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Burukanli, M. and N. Yumu¸sak, 2024 Tfradmcov: a robust transformer encoder based model with adam optimizer algorithm for covid-19 mutation prediction. Connection Science 36: 2365334.
Çiçek, Ö., A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, 2016 3d u-net: Learning dense volumetric segmentation from sparse annotation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 424–432.
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Kunduracioglu, I., 2024b Utilizing resnet architectures for identification of tomato diseases. Journal of Intelligent Decision Making and Information Science 1: 104–119.
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U-Net-Based Models for Precise Brain Stroke Segmentation

Year 2025, Volume: 7 Issue: 1, 50 - 60, 31.03.2025

Suat İnce , Ismail Kunduracioglu , Bilal Bayram , Ishak Pacal

https://doi.org/10.51537/chaos.1605529

Cited By: 26

Abstract

Ischemic stroke, a widespread neurological condition with a substantial mortality rate, necessitates accurate delineation of affected regions to enable proper evaluation of patient outcomes. However, such precision is complicated by factors like variable lesion sizes, noise interference, and the overlapping intensity characteristics of different tissue structures. This research addresses these issues by focusing on the segmentation of Diffusion Weighted Imaging (DWI) scans from the ISLES 2022 dataset and conducting a comparative assessment of three advanced deep learning models: the U-Net framework, its U-Net++ extension, and the Attention U-Net. Applying consistent evaluation criteria specifically, Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and recall the Attention U-Net emerged as the superior choice, establishing record high values for IoU (0.8223) and DSC (0.9021). Although U-Net achieved commendable recall, its performance lagged behind that of U-Net++ in other critical measures. These findings underscore the value of integrating attention mechanisms to achieve more precise segmentation. Moreover, they highlight that the Attention U-Net model is a reliable candidate for medical imaging tasks where both accuracy and efficiency hold paramount importance, while U Net and U Net++ may still prove suitable in certain niche scenarios.

Keywords

Medical image analysis , Ishemic stroke , Brain stroke , Segmentation , U-Net , Deep learning

References

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Kunduracioglu, I. and I. Pacal, 2024 Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases. Journal of Plant Diseases and Protection .
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Pacal, I., 2025 Investigating deep learning approaches for cervical cancer diagnosis: a focus on modern image-based models. European Journal of Gynaecological Oncology 46: 125–141.
Pacal, I., I. Kunduracioglu, M. H. Alma, M. Deveci, S. Kadry, et al., 2024 A systematic review of deep learning techniques for plant diseases. Artificial Intelligence Review 57: 304.
Paçal, I. and I. Kunduracıo˘ glu, 2024 Data-efficient vision transformer models for robust classification of sugarcane. Journal of Soft Computing and Decision Analytics 2: 258–271.
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Schlemper, J., O. Oktay, M. Schaap, M. Heinrich, B. Kainz, et al., 2019 Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Analysis 53: 197–207.
The GBD, . L. R. O. S. C., 2018 Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016. New England Journal of Medicine 379: 2429–2437.
Tomita, N., S. Jiang, M. E. Maeder, and S. Hassanpour, 2020 Automatic post-stroke lesion segmentation on mr images using 3d residual convolutional neural network. NeuroImage: Clinical 27: 102276.
Tursynova, A. and B. Omarov, 2021 3d u-net for brain stroke lesion segmentation on isles 2018 dataset. In 2021 16th International Conference on Electronics Computer and Computation (ICECCO), pp. 1–4.
van Rijsbergen, C. J., 1979 Information Retrieval. Butterworth. Verclytte, S., R. Gnanih, S. Verdun, T. Feiweier, B. Clifford, et al., 2023 Ultrafast mri using deep learning echoplanar imaging for a comprehensive assessment of acute ischemic stroke. European Radiology 33: 3715–3725.
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Details

Primary Language	English
Subjects	Software Engineering (Other), Biomedical Engineering (Other)
Journal Section	Research Articles
Authors	Suat İnce 0000-0002-2156-1347 Ismail Kunduracioglu 0000-0002-4270-2153 Bilal Bayram 0000-0003-1952-2581 Ishak Pacal 0000-0001-6670-2169
Publication Date	March 31, 2025
Submission Date	December 22, 2024
Acceptance Date	January 29, 2025
Published in Issue	Year 2025 Volume: 7 Issue: 1

Cite

APA	İnce, S., Kunduracioglu, I., Bayram, B., Pacal, I. (2025). U-Net-Based Models for Precise Brain Stroke Segmentation. Chaos Theory and Applications, 7(1), 50-60. https://doi.org/10.51537/chaos.1605529