Exploring GANs and diffusing models for medical Image data augmentation / Ikram El Houiti

Public ISBD Titre : Exploring GANs and diffusing models for medical Image data augmentation Type de document : document multimédia Auteurs : Ikram El Houiti, Auteur ; Amina Habib, Auteur ; Younes Guellouma, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2025 Importance : 60 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Data sience and artificial intelligence Langues : Anglais Mots-clés : Medical Imaging  GANs  Diffusion Models  Data Augmentation  Image Analysis  Artificial Intelligence  Generative models  Deep`Learning  Image synthesis  Chest X-Ray Résumé : The scarcity of medical images remains a major challenge in the development of accurate and reliable deep learning models for medical image analysis. This thesis explores the use of generative models specifically Generative Adversarial Networks (GANs) and Diffusion Models to synthesize realistic chest X-ray images for data`augmentation. Different generative models were implemented and evaluated such as DCGAN, WGAN-GP, ProGAN, StyleGAN3, DDPM,`and DDIM. All models were trained to generate grayscale chest X-ray images at a resolution of 64×64 pixels. The quality of`the generated images was assessed using both quantitative metrics, such as the Fréchet Inception Distance (FID), and qualitative evaluation through a Visual Turing Test (VTT) conducted by a radiologist. Results showed that diffusion models, particularly DDIM and DDPM, produced the most realistic images, in`the normal and pneumonia classes. Interestingly, DCGAN showed unexpectedly strong performance when generating Normal chest X-ray images. The findings demonstrate that generative models, especially diffusion-based models can effectively contribute to expanding medical datasets and improving deep learning performance, although computational limitations and resolution constraints remain challenges note de thèses : Mémoire de master en informatique Exploring GANs and diffusing models for medical Image data augmentation [document multimédia] / Ikram El Houiti, Auteur ; Amina Habib, Auteur ; Younes Guellouma, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 60 p. + 1 disque optique numérique (CD-ROM). Option : Data sience and artificial intelligence Langues : Anglais Mots-clés : Medical Imaging  GANs  Diffusion Models  Data Augmentation  Image Analysis  Artificial Intelligence  Generative models  Deep`Learning  Image synthesis  Chest X-Ray Résumé : The scarcity of medical images remains a major challenge in the development of accurate and reliable deep learning models for medical image analysis. This thesis explores the use of generative models specifically Generative Adversarial Networks (GANs) and Diffusion Models to synthesize realistic chest X-ray images for data`augmentation. Different generative models were implemented and evaluated such as DCGAN, WGAN-GP, ProGAN, StyleGAN3, DDPM,`and DDIM. All models were trained to generate grayscale chest X-ray images at a resolution of 64×64 pixels. The quality of`the generated images was assessed using both quantitative metrics, such as the Fréchet Inception Distance (FID), and qualitative evaluation through a Visual Turing Test (VTT) conducted by a radiologist. Results showed that diffusion models, particularly DDIM and DDPM, produced the most realistic images, in`the normal and pneumonia classes. Interestingly, DCGAN showed unexpectedly strong performance when generating Normal chest X-ray images. The findings demonstrate that generative models, especially diffusion-based models can effectively contribute to expanding medical datasets and improving deep learning performance, although computational limitations and resolution constraints remain challenges note de thèses : Mémoire de master en informatique

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