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https://dspace.univ-ouargla.dz/jspui/handle/123456789/34963
Title: | Deep learning based 3D brain tumor segmentation |
Authors: | Khaldi, bilale Bouldjemar, Sirine Bammoune, Chouaib |
Keywords: | MRI Multi-modals segmentation Deep learning and U-net architecture |
Issue Date: | 2023 |
Publisher: | UNIVERSITY OF KASDI MERBAH OUARGLA |
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. |
URI: | https://dspace.univ-ouargla.dz/jspui/handle/123456789/34963 |
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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