Faculté des mathématiques et de l'informatique
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Item Brain Tumor Detection Using U-Net and SVM(university of bordj bou arreridj, 2025) BENGUEZZOUMohammed; BENYAHIAOUI Mohamed AssilBrain tumors, particularly gliomas, pose a significant clinical challenge, requiring both precise localization and accurate grading to guide treatment. Accurate segmentation of tumor regions is a critical first step, enabling meaningful analysis and interpretation of the affected areas. In this project, we present a hybrid framework that first segments tumor regions in brain Magnetic Resonance Imaging (MRI) scans using a U-Net model trained on the Brain Tumor Segmentation dataset, and then classifies these regions as Low-Grade or High-Grade Gliomas with a Support Vector Machine (SVM) model based on features extracted from the segmented masks. On the held-out test set, our U-Net achieved an accuracy of 99.3%, while the SVM classifier delivered an overall accuracy of 93%.Item Brain MRI Image Segmentation Using YOLO and U-Net Deep Learning Models(university of bordj bou arreridj, 2025) Dhikra YOUSFIThe 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.