MERDJI, SaidaREBIAI, Soria2024-10-312024-10-312024MM/846http://10.10.1.6:4000/handle/123456789/5671TThe accurate diagnosis of contemporary diseases heavily relies on the processing of medical images. This study introduces an interesting approach for automated detection of brain tumors from magnetic resonance imaging (MRI) using the deep learning model ResNet50. This model, renowned for its ability to extract complex features from images, is deployed to analyze brain MRI images and accurately identify the presence of tumors. The data used in this study include MRI images containing tumors. We compared our approach to other methods using criteria such as precision, recall, and F1 score. The proposed model, ResNet50, achieved a detection accuracy of 98%, demonstrating its effectiveness in detecting brain tumors from MRI images. These results highlight the potential of the ResNet50 model to improve early and accurate detection of brain tumors in images.frImage IRM, ResNet50, CNN, Apprentissage en profondeur, traitement des images ,cerveau,Tumeurs cérébralesMRI, ResNet50, CNN, Deep Learning, Brain Tumors, AccuracyThesis