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

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dc.contributor.author NOUIRI, Lahcene
dc.contributor.author DERRECHE, Seddik
dc.date.accessioned 2024-10-21T09:51:07Z
dc.date.available 2024-10-21T09:51:07Z
dc.date.issued 2024
dc.identifier.issn MM/838
dc.identifier.uri https://dspace.univ-bba.dz:443/xmlui/handle/123456789/5657
dc.description.abstract The 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.language.iso fr en_US
dc.publisher UNIVERSITY BBA en_US
dc.subject MRI, U-net, segmentation, Deep learning, Brain tumors en_US
dc.title Segmentation des tumeurs cérébrales dans des images IRM par la méthode U-Net en_US
dc.type Thesis en_US


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