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Résumé : Brain tumors are a serious illness that needs to be diagnosed as soon as possible. Convolutional neural networks (CNNs) can automate parts of medical image analysis, as shown by recent developments in deep learning. This could help clinicians and speed up diagnosis. In this work, we describe a fully reproducible pipeline that uses transfer learning to classify brain tumors into four categories: glioma, meningioma, pituitary, and healthy. We make use of an MRI dataset with over 7,000 labeled images that is made publicly available by Mendeley Data. To produce four classes, our method uses a ResNet-18 model that was pretrained on ImageNet and has had its last layer swapped out. After normalizing and resizing each MRI scan to 224 x 224 pixels, standard training methods (such as cross-entropy loss and Adam optimizer) are used. Instead of emphasizing particular outcomes, we emphasize reproducibility and a clear methodology, providing examples of data preprocessing, model setup, and sample visualizations. This framework shows how pre-trained CNNs can be effectively modified to classify brain tumors, cutting down on training time without sacrificing performance.