@article{article_1694369, title={Hybrid Deep Learning and Reinforcement Learning Approach for Brain Tumor Classification from MRI Images}, journal={Süleyman Demirel University Faculty of Arts and Science Journal of Science}, volume={20}, pages={206–221}, year={2025}, DOI={10.29233/sdufeffd.1694369}, author={Saraç, Çiğdem and Arıkan Arıbal, Seda and Üncü, Yiğit Ali}, keywords={Brain tumor, MRI classification, Deep learning, Reinforcement learning, CNN models}, abstract={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.}, number={2}, publisher={Süleyman Demirel University}