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https://dspace.univ-ouargla.dz/jspui/handle/123456789/34963
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DC Field | Value | Language |
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dc.contributor.author | Khaldi, bilale | - |
dc.contributor.author | Bouldjemar, Sirine | - |
dc.contributor.author | Bammoune, Chouaib | - |
dc.date.accessioned | 2023-11-07T09:26:30Z | - |
dc.date.available | 2023-11-07T09:26:30Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://dspace.univ-ouargla.dz/jspui/handle/123456789/34963 | - |
dc.description.abstract | Brain tumor segmentation plays a crucial role in medical image analysis and assists in the diagnosis, treatment planning, and monitoring of brain tumor patients. However, accurately segmenting brain tumors from multi-modal medical images remains a challenging task due to the complex and heterogeneous nature of tumors, tissues. In thisthesis,weproposea3DU-netdeeplearningmodelforthepurpose of having accurate 3D brain tumor segmentation from multi-modality data to take advantage of different levels of information that exist in theMRIsequences,inadditiontoreducingthediagnosistimeandhaving an automated process to address the problem from like-real data and scale down the human bias when dealing with such a sensitive task. The 3D U-net model is trained on the BRATS2020 data-set and evaluated with segmentation volumetric metrics. The model showed promising results on the majority of the test data 63 % that reached high IOU score for the whole tumor region and showed a tolerance to variationafterapplyingthetestonnoisydata. However,itmissclassifiedthe37%ofthetestdatabecauseoftheimbalancebetweenclasses. as a recommendation to tackle this problem; a customized approach to deal with imbalance conditions can be proposed. | en_US |
dc.language.iso | en | en_US |
dc.publisher | UNIVERSITY OF KASDI MERBAH OUARGLA | en_US |
dc.subject | MRI Multi-modals | en_US |
dc.subject | segmentation | en_US |
dc.subject | Deep learning and U-net architecture | en_US |
dc.title | Deep learning based 3D brain tumor segmentation | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Département d'informatique et technologie de l'information - Master |
Files in This Item:
File | Description | Size | Format | |
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BOULJEMAR-BAMMOUNE.pdf | 2,99 MB | Adobe PDF | View/Open |
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