| Titre : | A Computer-Aided Diagnosis System for the Diagnosis of Thyroid Cancer |
| Auteurs : | Abdessalam Kamel Eddine Rezig, Auteur ; Sihem SAHLI, Directeur de thèse |
| Type de document : | Mémoire magistere |
| Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2025 |
| Format : | 1 vol. (77 p.) / ill.couv.ill.encoul |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Résumé : |
Artificial intelligence (AI) has witnessed a significant surge in demand across various scientific fields, offering intelligent and innovative solutions to complex problems particularlyin the healthcare sector. Thyroid cancer, one of the most prevalent endocrine malignancies,presents a major challenge for early diagnosis and effective treatment, both globally and in countries like Algeria. Among the available diagnostic techniques, ultrasound imaging remains the most commonly used method due to its affordability, accessibility,and non-invasive nature. However, its diagnostic accuracy largely depends on the physician’s experience and their ability to interpret subtle features in the images. This subjectivity can sometimes result in misinterpretation, inconclusive results, or unnecessary biopsies. To overcome these limitations, our research aims to develop an AI-based approach to improve the early detection of thyroid cancer. By enhancing diagnostic accuracyand reducing human error, this intelligent system aspires to provide a more reliable,efficient, and safer tool for early diagnosis, especially in patients at high risk. |
| Sommaire : |
Abstract ii Key words v General Introduction 1 1 Thyroid Anatomy and Thyroid Cancer Overview 4 1.1 Anatomy and Function of the Thyroid Gland . . . . . . . . . . . . . . . . 5 1.2 Thyroid Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 Types of Thyroid Cancer . . . . . . . . . . . . . . . . . . . . . . . 6 1.3 Symptoms and Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.1 Symptoms of Thyroid Cancer . . . . . . . . . . . . . . . . . . . . 7 1.3.2 Risk Factors for Thyroid Cancer . . . . . . . . . . . . . . . . . . . 7 1.4 Conventional Diagnostic Methods . . . . . . . . . . . . . . . . . . . . . . 8 1.5 Importance of Early Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Computer-Aided Diagnosis (CAD) Systems 11 2.1 Definition and Purpose of CAD . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 General Architecture of a CAD System . . . . . . . . . . . . . . . . . . . 12 2.3 CAD Applications in Oncology . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Limitations and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3 Literature Review on CAD for Thyroid Cancer Diagnosis 15 3.1 Imaging Modalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.1 Common Imaging Modalities . . . . . . . . . . . . . . . . . . . . . 16 3.2 Image Processing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.3 Use of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3.1 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4 Performance Comparison of Existing Approaches . . . . . . . . . . . . . 23 3.4.1 Performance measures used . . . . . . . . . . . . . . . . . . . . . 24 3.4.2 Comparison of the performance of existing methods . . . . . . . . 27 3.5 Critical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4 Design of the Proposed CAD System 29 4.1 Proposed study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2.1 System workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.3 System components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.3.1 Data Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.3.2 Dataset Preparation . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.3.3 Segmentation Model . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.3.4 Classification Model . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.4 Software Architecture of the System . . . . . . . . . . . . . . . . . . . . 35 5 Implementation and Experimental Results 38 5.1 Tools and Development Environment . . . . . . . . . . . . . . . . . . . . 39 5.1.1 Programming language and library . . . . . . . . . . . . . . . . . 39 5.1.2 Required programs . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.2 Dataset Type Description . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.3 Data Spliting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.4 Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.5 Trained Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.6 Performance Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . 48 5.6.1 Segmentation Model Performance Evaluation Metrics . . . . . . . 48 5.6.2 Classification Model Performance Evaluation Metrics . . . . . . . 52 5.6.3 Metrics Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.7 Comparison with Existing Methods . . . . . . . . . . . . . . . . . . . . . 53 5.7.1 Comparing the performance of existing segmentation methods with the proposed method . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.7.2 Comparing the performance of Existing Classification Methods with the proposed method . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.8 Discussion and Interpretation of Results . . . . . . . . . . . . . . . . . . 54 Generale Conclusion 57 A Parts of the code 58 A.1 Import library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 A.2 Preprocessing Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 A.3 dataloader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 A.3.1 Train function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 A.3.2 Validation Function . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 |
| Type de document : | Mémoire master |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| MINF/950 | Mémoire master | bibliothèque sciences exactes | Consultable |
