| Titre : | Efficient classification of ischemic stroke lesions |
| Auteurs : | roufaida Khebbache, Auteur ; Rachida Saouli, Directeur de thèse |
| Type de document : | Monographie imprimée |
| Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2019 |
| Format : | 1 vol. (50 p.) / ill. / 29 cm |
| Langues: | Anglais |
| Résumé : |
Stroke is one of the leading causes of death and disability. Therefore the manual segmentation of it's lesions is time-consuming, automatic segmentation methods of the stroke has recently extracted attentions. One of the successful automatic methods that achieve state of the art is the deep learning (convolutional neural network), this method is used in multiple domains such that image recognition,Medical context, natural language processing etc ...The need for a short prediction time and an accurate segmentation is a challenge to work on that's why all the present and the future works are focusing on the variety architectures witch get the score and to achieve the state of the art. We intent in this work to build a Convoultional neural network for the task of the segmentation,our contribution is that we based our model on the Auto-encoder network by inspiring from their main idea and by using a CNN layers with multispectral MRI images to improve the robustness of our model. |
| Sommaire : |
1 General introduction 1 2 State of the art: Segmentation methods of MRI images 3 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 De?nition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2.1 Medical Image Segmentation . . . . . . . . . . . . . . . . . . . 4 2.3 Elementary notions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3.1 The Segmentation notion . . . . . . . . . . . . . . . . . . . . . 4 2.3.2 IMAGE SEGMENTATION TECHNIQUES . . . . . . . . . . 5 2.4 Deep Learning approach . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4.1 Arti?cial neural networks . . . . . . . . . . . . . . . . . . . . . 9 2.4.2 Deep learning approach . . . . . . . . . . . . . . . . . . . . . 14 2.5 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.6 Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3 Convolutional Neural Network for Stroke Lesion Segmentation and Implementation 24 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.1.1 General Architecture . . . . . . . . . . . . . . . . . . . . . . . 25 3.1.2 Model on Layers . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3 Results and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4 General Conclusion 48 Bibliography 49 |
| Type de document : | Mémoire master |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| MINF/478 | Mémoire master | bibliothèque sciences exactes | Consultable |
