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Élément Dublin Core | Valeur | Langue |
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dc.contributor.author | KACEM, Mohamed | - |
dc.date.accessioned | 2024-07-21T13:13:04Z | - |
dc.date.available | 2024-07-21T13:13:04Z | - |
dc.date.issued | 2024-06 | - |
dc.identifier.uri | http://dspace.univ-tiaret.dz:80/handle/123456789/14745 | - |
dc.description.abstract | This thesis explores the diagnosis and treatment of brain tumors through advanced imaging techniques and deep learning algorithms. Brain cancer, a severe condition affecting the central nervous system, requires precise diagnostic methods for effective treatment. This work focuses on the segmentation of brain tumors from MRI images using state-of-the-art deep learning models. The proposed method integrates pre-trained convolutional neural networks, improving segmentation accuracy and robustness. Our results demonstrate significant advancements in the accuracy and efficiency of brain tumor diagnosis and provide a foundation for future research in this critical medical field. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Ibn Khaldoun University | en_US |
dc.subject | Brain Tumor | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Convolutional Neural Networks | en_US |
dc.subject | Medical Imaging | en_US |
dc.title | Brain Tumor Segmentation Using Neural Networks convolutional (CNN) | en_US |
dc.type | Thesis | en_US |
Collection(s) : | Master |
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TH.M.INF.2024.04.pdf | 874,25 kB | Adobe PDF | Voir/Ouvrir |
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