Hybrid Deep Learning and Reinforcement Learning Approach for Brain Tumor Classification from MRI Images

Brain cancer, resulting from abnormal tumor growth in brain tissue, requires accurate and timely diagnosis. Although MRI plays a crucial role, manual interpretation is prone to errors and delays. To address this, we propose a hybrid system combining deep learning (VGG16, ResNet50, DenseNet201) with reinforcement learning (Q-learning) for brain tumor classification. Using three distinct MRI datasets within MATLAB, the models achieved high classification accuracies: 97.45% (VGG16), 96.06% (ResNet50), and 96.93% (DenseNet201). The integration of reinforcement learning improved decision-making and interpretability. Additionally, a user-friendly interface was developed to support clinical decision-making. This study demonstrates that combining deep learning with reinforcement learning enhances model adaptability, offering a more reliable and effective diagnostic approach.