University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Auteur hadjer Ouarem |
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Titre : Deep Learning Model for Brain Tumor Radiogenomic Classification Type de document : texte imprimé Auteurs : hadjer Ouarem, Auteur ; aymen mohamed Sraouia, Auteur ; khaled Nasri, Directeur de thèse Année de publication : 2022 Importance : 1 vol (67 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Résumé :
For malignant brain tumor (Glioblastoma), known as the worst prognosis, with median
survival being less than a year, recent medical research demonstrates that the presence
of a specific genetic sequence in the tumor known as MGMT promoter methylation has
been shown to be a favorable prognostic factor and a strong predictor of responsiveness to
chemotherapy. The problem is that traditional methodology of surgery to extract a sample to be
analyzed is very complicated for brain tumors cases, and takes long time.
In this work we will explore the efficiency of Deep Learning based methodology to detect the
existence of specific genomic sequences from MRI images. This alternative can be very useful
and can help many cases to be treated. We have used many pretrained models and many images
sequences to realize our experiences, to improve our models and determine which image sequence
is the best to detect MGMT genome from MRI data.Côte titre : MAI/0584 En ligne : https://drive.google.com/file/d/1vIJffysvjMQk-H2jqZA5t7HgVVIUBnUb/view?usp=share [...] Format de la ressource électronique : Deep Learning Model for Brain Tumor Radiogenomic Classification [texte imprimé] / hadjer Ouarem, Auteur ; aymen mohamed Sraouia, Auteur ; khaled Nasri, Directeur de thèse . - 2022 . - 1 vol (67 f .) ; 29cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Résumé :
For malignant brain tumor (Glioblastoma), known as the worst prognosis, with median
survival being less than a year, recent medical research demonstrates that the presence
of a specific genetic sequence in the tumor known as MGMT promoter methylation has
been shown to be a favorable prognostic factor and a strong predictor of responsiveness to
chemotherapy. The problem is that traditional methodology of surgery to extract a sample to be
analyzed is very complicated for brain tumors cases, and takes long time.
In this work we will explore the efficiency of Deep Learning based methodology to detect the
existence of specific genomic sequences from MRI images. This alternative can be very useful
and can help many cases to be treated. We have used many pretrained models and many images
sequences to realize our experiences, to improve our models and determine which image sequence
is the best to detect MGMT genome from MRI data.Côte titre : MAI/0584 En ligne : https://drive.google.com/file/d/1vIJffysvjMQk-H2jqZA5t7HgVVIUBnUb/view?usp=share [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0584 MAI/0584 Mémoire Bibliothéque des sciences Anglais Disponible
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