| Titre : |
Machine learning and deep learning methods for cancer prediction and responses to its treatment |
| Type de document : |
document électronique |
| Auteurs : |
Wissal Achour, Auteur ; Feriel Boudjatit, Auteur ; Nour El Houda Benalia, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2024 |
| Importance : |
1 fichier PDF (7 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Electronique : Alger, École Nationale Polytechnique : 2024
Bibliogr. p. 129 - 140 . Webographie p. 141 - 143 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Cancer
Brain tumor
Cancer Drug Response
MRI
Machine learning
Deep learning
Classification |
| Index. décimale : |
PN00624 |
| Résumé : |
Cancer remains a significant global health challenge, affecting individuals of all ages. Early detection and personalized treatment are crucial as they significantly improve prognosis and treatment outcomes. Recent advancements in machine learning (ML) and deep learning (DL) methods, have shown considerable promise in enhancing cancer detection through medical image analysis and predicting patient-specific drug responses. This study focuses on the classi- fication of Gliomas, a type of brain tumor, into Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG) by proposing an end-to-end tumor grading model that performs on MRI slices. Additionally, it explores the development of a predictive model for cancer drug response by leveraging drug molecular data and clinical cell line information. |
Machine learning and deep learning methods for cancer prediction and responses to its treatment [document électronique] / Wissal Achour, Auteur ; Feriel Boudjatit, Auteur ; Nour El Houda Benalia, Directeur de thèse . - [S.l.] : [s.n.], 2024 . - 1 fichier PDF (7 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Electronique : Alger, École Nationale Polytechnique : 2024
Bibliogr. p. 129 - 140 . Webographie p. 141 - 143 Langues : Anglais ( eng)
| Mots-clés : |
Cancer
Brain tumor
Cancer Drug Response
MRI
Machine learning
Deep learning
Classification |
| Index. décimale : |
PN00624 |
| Résumé : |
Cancer remains a significant global health challenge, affecting individuals of all ages. Early detection and personalized treatment are crucial as they significantly improve prognosis and treatment outcomes. Recent advancements in machine learning (ML) and deep learning (DL) methods, have shown considerable promise in enhancing cancer detection through medical image analysis and predicting patient-specific drug responses. This study focuses on the classi- fication of Gliomas, a type of brain tumor, into Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG) by proposing an end-to-end tumor grading model that performs on MRI slices. Additionally, it explores the development of a predictive model for cancer drug response by leveraging drug molecular data and clinical cell line information. |
|
PN00100 
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