Brain Tumor Detection Using Deep Learning

This study examines the effectiveness of deep learning approaches in detecting brain tumors. Early diagnosis of brain tumors is critical for improving patients’ quality of life and optimizing treatment processes. In this study, the MobileNetV2 model was used for feature extraction from Magnetic Resonance Imaging (MRI) data, and the classification process was carried out using the $\mathbf{k}$-Nearest Neighbors ($\mathbf{k}$-NN) algorithm. Experimental results show that the MobileNetV2 model achieved an accuracy rate of $\mathbf{8 8. 2 4 \%}$. These findings highlight the potential of AI-assisted systems as fast and reliable tools for brain tumor detection.