Deep Learning Models for Colorectal Cancer Detection and Classification / Mohamed Islem Belhaddad  

Public ISBD Titre : Deep Learning Models for Colorectal Cancer Detection and Classification Type de document : texte imprimé Auteurs : Mohamed Islem Belhaddad, Auteur ; Ouail Saim, Auteur ; Abdelouahab Moussaoui, Directeur de thèse Année de publication : 2023 Importance : 1 vol (119 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Colorectal cancer Deep learning Index. décimale : 004 - Informatique Résumé : Colorectal cancer is a leading cause of cancer-related deaths worldwide, and early detection is crucial for improving patient outcomes. In recent years, deep learning has emerged as a promising approach for automated cancer detection and classification. In this thesis, we propose a framework for using deep learning models to detect and classify colorectal cancer based on histopathological images. We investigate the performance of different deep learning architectures, including convolutional neural networks, on a dataset of colorectal cancer images. Our results demonstrate the potential of deep learning models for accurately detecting and classifying colorectal cancer, with an accuracy of over 99.2% achieved in some cases. We also discuss the limitations and challenges of using deep learning for cancer detection and provide recommendations for future research. Overall, our study highlights the potential of deep learning as a powerful tool for improving cancer diagnosis and treatment. Côte titre : MAI/0710 En ligne : https://drive.google.com/file/d/1QClwTrsGi4-9LMu7MxWPhniE-F2cAjYz/view?usp=drive [...] Format de la ressource électronique : pdf Deep Learning Models for Colorectal Cancer Detection and Classification [texte imprimé] / Mohamed Islem Belhaddad, Auteur ; Ouail Saim, Auteur ; Abdelouahab Moussaoui, Directeur de thèse . - 2023 . - 1 vol (119 f .) ; 29cm. Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Colorectal cancer Deep learning Index. décimale : 004 - Informatique Résumé : Colorectal cancer is a leading cause of cancer-related deaths worldwide, and early detection is crucial for improving patient outcomes. In recent years, deep learning has emerged as a promising approach for automated cancer detection and classification. In this thesis, we propose a framework for using deep learning models to detect and classify colorectal cancer based on histopathological images. We investigate the performance of different deep learning architectures, including convolutional neural networks, on a dataset of colorectal cancer images. Our results demonstrate the potential of deep learning models for accurately detecting and classifying colorectal cancer, with an accuracy of over 99.2% achieved in some cases. We also discuss the limitations and challenges of using deep learning for cancer detection and provide recommendations for future research. Overall, our study highlights the potential of deep learning as a powerful tool for improving cancer diagnosis and treatment. Côte titre : MAI/0710 En ligne : https://drive.google.com/file/d/1QClwTrsGi4-9LMu7MxWPhniE-F2cAjYz/view?usp=drive [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0710	MAI/0710	Mémoire	Bibliothéque des sciences	Anglais	Disponible Disponible

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