University Sétif 1 FERHAT ABBAS Faculty of Sciences
Détail de l'auteur
Auteur Imane hibet errahmene Aissaoui |
Documents disponibles écrits par cet auteur



Deep Learning Models for Predicting Recurrence in Head and Neck Cancer / Imane hibet errahmene Aissaoui
Titre : Deep Learning Models for Predicting Recurrence in Head and Neck Cancer Type de document : document électronique Auteurs : Imane hibet errahmene Aissaoui, Auteur ; Seif eddine Chouaba, Directeur de thèse Editeur : Setif:UFA Année de publication : 2024 Importance : 1 vol (54 f.) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Physique Mots-clés : Head and neck cancer
Recurrence Prediction
Deep learning
Medical Imaging
Computed Tomography
Convolutional Neural NetworksIndex. décimale : 530 - Physique Résumé :
This master thesis aims to investigate and develop deep learning models for predicting head and neck cancer recurrence. This latter is a major cause of morbidity and treatment failure and poses a significant challenge in healthcare, which demands accurate prognostic tools for effective patient management. Deep learning, a subset of artificial intelligence, has shown promising results in various medical applications, including cancer recurrence prediction.
The primary goal of this research is to develop a range of deep learning models based on diverse convolutional neural networks architectures and subsequently conduct a thorough comparative analysis to evaluate their performance in predicting head and neck cancer recurrence. By systematically assessing the strengths and limitations of each architecture, this study aims to provide valuable insights into the development of optimal deep learning approaches for accurate recurrence prediction.
The anticipated contributions of this thesis include the identification of the most effective deep learning architecture for predicting head and neck cancer recurrence, providing clinicians with a valuable tool for personalized treatment planning. Additionally, insights gained from the comparative analysis may inform future developments in deep learning applications within the field of oncology.Note de contenu : Sommaire
CHAPITRE01: HEAD AND NECK CANCER
1. BACKGROUND ............................................................................................ 3
2. HEAD AND NECK CANCER RECCURENCE ....................................................... 4
2.1 Head and neck cancer ............................................................................................................ 4
2.2 Head and neck cancer recurrence ......................................................................................... 4
3. HEAD AND NECK CANCER RECCURENCE PREDICTION APPROACHES .............. 5
3.1 Traditional statistical models ................................................................................................. 6
3.2 Machine Learning (ML) ......................................................................................................... 6
3.3 Deep Learning (DL): .............................................................................................................. 8
4 CONCLUSION .............................................................................................. 9
CHAPITRE 02: DEEP LEARNING: BASICS AND CONVOLUTION NEURAL NETWORK
1 BACKGROUND ............................................................................................10
2. FOUNDATIONS OF DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS (CNNs) ........................................................................................... 11
2.1 Artificial Intelligence (AI) .................................................................................. 11
2.2 Machine Learning (ML) .......................................................................................................... 11
2.3 Deep learning (DL) ................................................................................................................ 12
3. LEARNING APPROACHES AND ARCHITECTURES ................................................. 13
3.1 Supervised Learning ............................................................................................................ 13
3.2 Unsupervised learning ......................................................................................................... 14
3.3. Semi-Supervised learning ................................................................................................... 14
4. CONVOLUTIONAL NEURAL NETWORKS ......................................................... 15
4.1 Components of convolutional neural networks ................................................................... 16
4.1.1 Input Layer. ................................................................................................................... 16
4.1.2 Convolution layer .......................................................................................................... 16
4.1.3 Pooling layer .................................................................................................................. 16
4.1.4 Flatten Layer ..................................................................................................................17
4.1.5 Fully-connected layer .....................................................................................................17
4.1.6 Output Layer ..................................................................................................................17
4.2 CNN workflow in medical imaging classification………………………………………………………......17
5. CHALLENGES AND LIMITATIONS OF DL MODELS IN HEAD AND NECK CANCER RECCURENCE PREDICTION .............................................................................. 19
5.1. Data Quality and Quantity .............................................................................................. 19
5.2. Model overfitting ............................................................................................................ 20
5.3. Computation requirement .............................................................................................. 20
5.4 Ethical considerations ..................................................................................................... 20
6. CONCLUSION............................................................................................. 21
CHAPITRE 03: MATERIALS AND METHODS
1. BACKGROUND .......................................................................................... 22
2. MATERIALS IMPLEMENTATION & PREPA-RATION ....................................... 23
2.1 Implementation tools ........................................................................................................... 23
2.1.1 Python ........................................................................................................................... 23
2.1.2 Anaconda Navigator ..................................................................................................... 24
2.1.3 Libraries and frameworks ............................................................................................ 24
2.2 Dataset ................................................................................................................................ 25
2.3 Dataset pre-Processing ........................................................................................................ 25
3. MODEL SELECTION AND TRAINING ............................................................ 26
3.1 CNN model ........................................................................................................................... 27
3.2 Xception model .................................................................................................................... 28
3.3 VGG16 model ....................................................................................................................... 29
4. Evaluation metrics ........................................................................................ 31
4.1 Accuracy ............................................................................................................................... 32
4.2 Receiver operating characteristic curve (ROC) curve ......................................................... 32
4.3 Area Under the ROC Curve (AUC) ...................................................................................... 33
4.4 Sensitivity ............................................................................................................................ 34
4.5 Specificity ............................................................................................................................. 34
5. CONCLUSION ............................................................................................ 34
CHAPITRE04: EXPERIMENTS, DISCUSSION&RESULTS
1. BACKGROUND .......................................................................................... 35
2. RESULTS ................................................................................................... 36
3. DISCUSSION .............................................................................................. 38
3.1 CNN model ........................................................................................................................... 38
3.2 Xception model .................................................................................................................... 39
3.3 VGG16 model ....................................................................................................................... 41
4. COMPARISON OF THE THREE MODELS(CNN, VGG16 AND XCEPTION) ............ 43
5. COMPARISON OF THE CNN MODEL OF THIS STUDY WITH THE RADIOMICS BASED MODEL BY Vallières et al. .........................................................................47
6. CONCLUSION ..............................................................................................47
CONCLUSION&PERSEPECTIVE .................................................................. 49
Bibliography ............................................................................................... 50Côte titre : MAPH/0631 Deep Learning Models for Predicting Recurrence in Head and Neck Cancer [document électronique] / Imane hibet errahmene Aissaoui, Auteur ; Seif eddine Chouaba, Directeur de thèse . - [S.l.] : Setif:UFA, 2024 . - 1 vol (54 f.) ; 29 cm.
Langues : Anglais (eng)
Catégories : Thèses & Mémoires:Physique Mots-clés : Head and neck cancer
Recurrence Prediction
Deep learning
Medical Imaging
Computed Tomography
Convolutional Neural NetworksIndex. décimale : 530 - Physique Résumé :
This master thesis aims to investigate and develop deep learning models for predicting head and neck cancer recurrence. This latter is a major cause of morbidity and treatment failure and poses a significant challenge in healthcare, which demands accurate prognostic tools for effective patient management. Deep learning, a subset of artificial intelligence, has shown promising results in various medical applications, including cancer recurrence prediction.
The primary goal of this research is to develop a range of deep learning models based on diverse convolutional neural networks architectures and subsequently conduct a thorough comparative analysis to evaluate their performance in predicting head and neck cancer recurrence. By systematically assessing the strengths and limitations of each architecture, this study aims to provide valuable insights into the development of optimal deep learning approaches for accurate recurrence prediction.
The anticipated contributions of this thesis include the identification of the most effective deep learning architecture for predicting head and neck cancer recurrence, providing clinicians with a valuable tool for personalized treatment planning. Additionally, insights gained from the comparative analysis may inform future developments in deep learning applications within the field of oncology.Note de contenu : Sommaire
CHAPITRE01: HEAD AND NECK CANCER
1. BACKGROUND ............................................................................................ 3
2. HEAD AND NECK CANCER RECCURENCE ....................................................... 4
2.1 Head and neck cancer ............................................................................................................ 4
2.2 Head and neck cancer recurrence ......................................................................................... 4
3. HEAD AND NECK CANCER RECCURENCE PREDICTION APPROACHES .............. 5
3.1 Traditional statistical models ................................................................................................. 6
3.2 Machine Learning (ML) ......................................................................................................... 6
3.3 Deep Learning (DL): .............................................................................................................. 8
4 CONCLUSION .............................................................................................. 9
CHAPITRE 02: DEEP LEARNING: BASICS AND CONVOLUTION NEURAL NETWORK
1 BACKGROUND ............................................................................................10
2. FOUNDATIONS OF DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS (CNNs) ........................................................................................... 11
2.1 Artificial Intelligence (AI) .................................................................................. 11
2.2 Machine Learning (ML) .......................................................................................................... 11
2.3 Deep learning (DL) ................................................................................................................ 12
3. LEARNING APPROACHES AND ARCHITECTURES ................................................. 13
3.1 Supervised Learning ............................................................................................................ 13
3.2 Unsupervised learning ......................................................................................................... 14
3.3. Semi-Supervised learning ................................................................................................... 14
4. CONVOLUTIONAL NEURAL NETWORKS ......................................................... 15
4.1 Components of convolutional neural networks ................................................................... 16
4.1.1 Input Layer. ................................................................................................................... 16
4.1.2 Convolution layer .......................................................................................................... 16
4.1.3 Pooling layer .................................................................................................................. 16
4.1.4 Flatten Layer ..................................................................................................................17
4.1.5 Fully-connected layer .....................................................................................................17
4.1.6 Output Layer ..................................................................................................................17
4.2 CNN workflow in medical imaging classification………………………………………………………......17
5. CHALLENGES AND LIMITATIONS OF DL MODELS IN HEAD AND NECK CANCER RECCURENCE PREDICTION .............................................................................. 19
5.1. Data Quality and Quantity .............................................................................................. 19
5.2. Model overfitting ............................................................................................................ 20
5.3. Computation requirement .............................................................................................. 20
5.4 Ethical considerations ..................................................................................................... 20
6. CONCLUSION............................................................................................. 21
CHAPITRE 03: MATERIALS AND METHODS
1. BACKGROUND .......................................................................................... 22
2. MATERIALS IMPLEMENTATION & PREPA-RATION ....................................... 23
2.1 Implementation tools ........................................................................................................... 23
2.1.1 Python ........................................................................................................................... 23
2.1.2 Anaconda Navigator ..................................................................................................... 24
2.1.3 Libraries and frameworks ............................................................................................ 24
2.2 Dataset ................................................................................................................................ 25
2.3 Dataset pre-Processing ........................................................................................................ 25
3. MODEL SELECTION AND TRAINING ............................................................ 26
3.1 CNN model ........................................................................................................................... 27
3.2 Xception model .................................................................................................................... 28
3.3 VGG16 model ....................................................................................................................... 29
4. Evaluation metrics ........................................................................................ 31
4.1 Accuracy ............................................................................................................................... 32
4.2 Receiver operating characteristic curve (ROC) curve ......................................................... 32
4.3 Area Under the ROC Curve (AUC) ...................................................................................... 33
4.4 Sensitivity ............................................................................................................................ 34
4.5 Specificity ............................................................................................................................. 34
5. CONCLUSION ............................................................................................ 34
CHAPITRE04: EXPERIMENTS, DISCUSSION&RESULTS
1. BACKGROUND .......................................................................................... 35
2. RESULTS ................................................................................................... 36
3. DISCUSSION .............................................................................................. 38
3.1 CNN model ........................................................................................................................... 38
3.2 Xception model .................................................................................................................... 39
3.3 VGG16 model ....................................................................................................................... 41
4. COMPARISON OF THE THREE MODELS(CNN, VGG16 AND XCEPTION) ............ 43
5. COMPARISON OF THE CNN MODEL OF THIS STUDY WITH THE RADIOMICS BASED MODEL BY Vallières et al. .........................................................................47
6. CONCLUSION ..............................................................................................47
CONCLUSION&PERSEPECTIVE .................................................................. 49
Bibliography ............................................................................................... 50Côte titre : MAPH/0631 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAPH/0631 MAPH/0631 Mémoire Bibliothéque des sciences Anglais Disponible
Disponible