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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Master thesis : Diagnosis of neurodegenerative diseases with deep learning : The case of Alzheimer's disease

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Backès, Lucas ULiège
Promotor(s) : Phillips, Christophe ULiège ; Louppe, Gilles ULiège
Date of defense : 5-Sep-2022/6-Sep-2022 • Permalink : http://hdl.handle.net/2268.2/15982
Details
Title : Master thesis : Diagnosis of neurodegenerative diseases with deep learning : The case of Alzheimer's disease
Translated title : [fr] Diagnostic des maladies neurodégénératives avec du deep learning : le cas de la maladie d'Alzheimer
Author : Backès, Lucas ULiège
Date of defense  : 5-Sep-2022/6-Sep-2022
Advisor(s) : Phillips, Christophe ULiège
Louppe, Gilles ULiège
Committee's member(s) : Marée, Raphaël ULiège
Language : English
Number of pages : 65
Keywords : [fr] Deep learning
[fr] Machine learning
[fr] Alzheimer's disease
[fr] convolutional neural network (CNN)
[fr] Vision transformer (ViT)
[fr] classification
[fr] region of interest
[fr] ADNI
Discipline(s) : Engineering, computing & technology > Computer science
Name of the research project : diagnosis of neurodegenerative diseases with deep learning : the case of Alzheimer's disease
Target public : Researchers
Professionals of domain
Student
General public
Other
Complementary URL : https://github.com/bax24/Alzheimer-Classification
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil en science des données, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[fr] Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases in the world and the most common cause of dementia. In recent times, accurate and early detection of AD plays a vital role in patient care and further treatment. Lately, studies on AD diagnosis has attached a great significanceto artificial-based diagnostic algorithms. During this master thesis we explore
how deep learning models can handle neuroimages in order to identify andpredict the evolution of the disease. Different from the traditional machine learning algorithms, deep learning does not require manually extracted features but instead utilizes 3D image processing models to learn features for the diagnosis and the prognosis of AD. The contribution of this work relies on a
more rigorous preprocessing phase involving skull-stripping and intensity normalization of the medical images. The hippocampus, a brain area critical for learning and memory, is especially affected at early stages of Alzheimer’s disease. In some parts of this work, It will be used as a region of interest for our algorithms that will consist in convolutional neural networks, the typical image classifier models, and vision transformers, a novel deep learning architecture.


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Author

  • Backès, Lucas ULiège Université de Liège > Master ingé. civ. sc. don. à . fin.

Promotor(s)

Committee's member(s)

  • Marée, Raphaël ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes stochastiques
    ORBi View his publications on ORBi
  • Total number of views 136
  • Total number of downloads 416










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