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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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