Democratizing Neuro-Oncology: Real-Time Multi-Class Brain Tumor Localization in MRI Using YOLOv11 for Early Intervention

Early and precise detection of brain tumors from Magnetic Resonance Imaging (MRI) is vital for improving patient survival rates and guiding clinical interventions. This study proposes an ultra-fast, highly accurate multi-class brain tumor localization framework based on the state-of-the-art YOLOv11 architecture. Evaluated on a comprehensive dataset of 5,805 MRI scans across four distinct categories (glioma, meningioma, pituitary tumor, and no tumor), the proposed model achieves a mean Average Precision ([email protected]) of 97.04% and a real-time inference speed of 110 Frames Per Second (FPS). The findings demonstrate the viability of deploying lightweight deep learning models in neuro-oncology for rapid, accessible diagnostic support in clinical workflows.