Browsing by Author "Bouzidi Youssouf"

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    Deep Learning Architectures for Precise Segmentation of Brain Tumors in MRI Images.
    (University of Msila, 2025) Bouzidi Youssouf; Supervisor/ ATTALLAH Bilal
    This master’s thesis addresses the critical challenge of precise brain tumor segmentation in multi-modal MRI images. Manual segmentation, the current clinical standard, is hindered by its time-consuming nature and inter-observer variability. To overcome these limitations, this research investigates the application of advanced deep learning architectures for automated and accurate tumor delineation. The study systematically evaluates and compares a range of models, from a baseline U-Net to state-of-the-art architectures such as FPN, DeepLabV3+, and U-Net++, enhanced with pre-trained encoders like SE-ResNet50 and EfficientNet to leverage transfer learning. Furthermore, ensemble methods, specifically Voting and Stacking, were implemented to boost segmentation performance. The results demonstrate that the Feature Pyramid Network (FPN) with an EfficientNet- B4 backbone achieved the best performance among single models, with a Dice Score of 0.8235, offering an excellent balance between accuracy and efficiency. However, the Stacking Ensemble model proved superior to all individual models, attaining the highest Dice Score of 0.8263 by effectively combining predictions from diverse base models. This research concludes that while the Stacking Ensemble provides the optimal solution for maximizing accuracy, the FPN architecture stands as a highly competitive and practical alternative for clinical applications