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