Deep Learning-Based Segmentation of High-Grade Glioma Tumors: A Comparative Analysis of MRI-Based Models
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DOI:
https://doi.org/10.5281/zenodo.15038514Keywords:
Attention UNet, Brain Tumor Segmentation, Deep Learning, Medical Imaging, MRIAbstract
Brain tumors, particularly high-grade malignant gliomas, pose a significant public health
challenge due to their high lethality and impact on patients' quality of life. Recent advancements in deep
learning-based medical imaging techniques have revolutionized brain tumor detection and segmentation,
enabling more precise and automated diagnostic processes. This study evaluates the performance of
multiple deep learning architectures, including UNet, Attention UNet, ESNet, LEDNet, LinkNet, SQNet,
and SegNet, for brain tumor segmentation using MRI scans from the Gazi Brains 2020 dataset. The
models were assessed based on various performance metrics, including Intersection over Union (IoU),
sensitivity, accuracy, and specificity. The results indicate that Attention UNet achieved the highest
segmentation accuracy, demonstrating superior performance in delineating tumor regions. However, the
trade-off between segmentation accuracy and computational efficiency remains a critical consideration.
The findings underscore the potential of deep learning-based segmentation techniques in improving the
accuracy and efficiency of brain tumor diagnosis, paving the way for more effective clinical decision
making. Future research could explore hybrid architectures and multi-modal MRI integration to further
enhance segmentation precision while optimizing computational resources.
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