|
| Titre : |
Deep Learning for Segmentation and Pathology Analysis in Medical Imaging |
| Type de document : |
document électronique |
| Auteurs : |
yamina Azzi ; Mohand Tahar Kechadi, Directeur de thèse ; Abdelouaheb Moussaoui, Directeur de thèse |
| Editeur : |
Setif:UFA |
| Année de publication : |
2024 |
| Importance : |
1 vol (125 f.) |
| Format : |
29 cm |
| Langues : |
Français (fre) |
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Deep learning, Medical Images, Brain Tumor, Image segmentation, Image
Classification, Class imbalance |
| Index. décimale : |
004 Informatique |
| Résumé : |
Since the advent of deep learning, there has been a technological revolution, endowing
computers with remarkable abilities in learning, reasoning, thinking, and decisionmaking.
Deep learning has played a pivotal role in facilitating feature extraction,
particularly in the realm of computer vision, where it significantly contributes to
learning and recognizing image and video content. This impact extends to the
field of medical image research, as demonstrated in this thesis. The focus is on
the role of deep learning in the precise automatic segmentation of medical images,
specifically in segmenting brain glioma tumors and their associated sub-tumoral
tissue from multimodal MRI scans. The thesis presents various contributions aimed
at achieving accurate segmentation, including a comparative study between machine
learning-based methods and deep learning architectures. Additionally, it sheds light
on the substantial influence of class imbalance on image segmentation evaluation
metrics. Lastly, the thesis proposes a transfer learning-based method to construct a
model capable of classifying three types of brain tumors: Meningioma, glioma, and
pituitary tumors |
| Côte titre : |
DI/0080 |
| En ligne : |
http://dspace.univ-setif.dz:8888/jspui/handle/123456789/4418 |
Deep Learning for Segmentation and Pathology Analysis in Medical Imaging [document électronique] / yamina Azzi ; Mohand Tahar Kechadi, Directeur de thèse ; Abdelouaheb Moussaoui, Directeur de thèse . - [S.l.] : Setif:UFA, 2024 . - 1 vol (125 f.) ; 29 cm. Langues : Français ( fre)
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Deep learning, Medical Images, Brain Tumor, Image segmentation, Image
Classification, Class imbalance |
| Index. décimale : |
004 Informatique |
| Résumé : |
Since the advent of deep learning, there has been a technological revolution, endowing
computers with remarkable abilities in learning, reasoning, thinking, and decisionmaking.
Deep learning has played a pivotal role in facilitating feature extraction,
particularly in the realm of computer vision, where it significantly contributes to
learning and recognizing image and video content. This impact extends to the
field of medical image research, as demonstrated in this thesis. The focus is on
the role of deep learning in the precise automatic segmentation of medical images,
specifically in segmenting brain glioma tumors and their associated sub-tumoral
tissue from multimodal MRI scans. The thesis presents various contributions aimed
at achieving accurate segmentation, including a comparative study between machine
learning-based methods and deep learning architectures. Additionally, it sheds light
on the substantial influence of class imbalance on image segmentation evaluation
metrics. Lastly, the thesis proposes a transfer learning-based method to construct a
model capable of classifying three types of brain tumors: Meningioma, glioma, and
pituitary tumors |
| Côte titre : |
DI/0080 |
| En ligne : |
http://dspace.univ-setif.dz:8888/jspui/handle/123456789/4418 |
|
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