Résumé:
Brain tumors are a complex and dangerous form of cancer, where early
diagnosis is crucial to improving patients’ chances of survival. Traditionally,
doctors have relied on clinical examinations and medical imaging techniques
such as MRI. However, these methods, although effective, remain subjective
and dependent on human expertise, which can lead to variations in diagnoses.
The use of computer-aided diagnostic systems, notably via deep learning
techniques, makes the process more objective, reproducible and accurate.
These systems are based on convolutional neural networks (CNNs) capable of
detecting complex patterns in medical images.
In this work, we proposed a brain tumor classification system ,we exploit
transfer learning, where the ResNet50 model was used as a base model
reinforced by the Convolutional Block Attention Module (CBAM) . This
module enables the model to focus on the most relevant regions of the images,
thus improving tumor detection. We also applied data pretreatment techniques
to improve model robustness.
