Dhikra YOUSFI2025-11-052025MM/890https://dspace.univ-bba.dz/handle/123456789/957The precise detection of brain tumors is crucial in medical practice, and the use of au tomated segmentation techniques has the potential to improve this significantly. The paper presents a comparative evaluation of U-Net and YOLOv8 deep learning models for automatic brain tumor segmentation from magnetic resonance imaging (MRI). U-Net, with its ability to achieve pixel-level accuracy, performed superiorly in terms of Intersection over Union (IoU) and Dice coefficient, indicating its robustness in boundary delineation. In contrast, YOLOv8 had better precision and recall, thus being more appropriate for fast segmentation. The com parison was made with the standard metrics of precision, recall, IoU, Dice coefficient, and F1 score, providing a well-rounded evaluation. The findings show that U-Net is better at gener ating precise segmentation boundaries, whereas YOLOv8 performs better when it comes to detecting tumors quickly and accurately. This comparison provides important insights for the determination of the most appropriate model depending on specific application requirements.enBrain Tumor SegmentationU-NetYOLOv8Deep LearningMRIMedical Imaging.Thesis