Segmentation des tumeurs cérébrales dans des images IRM par la méthode U-Net

dc.contributor.authorNOUIRI, Lahcene
dc.contributor.authorDERRECHE, Seddik
dc.date.accessioned2024-10-21T09:51:07Z
dc.date.available2024-10-21T09:51:07Z
dc.date.issued2024
dc.description.abstractThe processing of medical images, especially those obtained by magnetic resonance imaging (MRI), is important for diagnosing certain diseases such as brain tumors. In this study, we propose a method to automatically detect these tumors using the U-Net network. Our approach is based on using this architecture to extract distinctive features from MRI images, which are then used for tumor detection. The results show that our U-Net-based model achieves a detection accuracy of 95%, demonstrating its effectiveness in this context. These results show the promising potential of using U-Net to improve the early and accurate detection of brain tumors from MRI images.en_US
dc.identifier.issnMM/838
dc.identifier.urihttp://10.10.1.6:4000/handle/123456789/5657
dc.language.isofren_US
dc.publisherUNIVERSITY BBAen_US
dc.subjectMRI, U-net, segmentation, Deep learning, Brain tumorsen_US
dc.titleSegmentation des tumeurs cérébrales dans des images IRM par la méthode U-Neten_US
dc.typeThesisen_US

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