Brain tumors, characterized by irregular cell growth in the brain or surrounding tissues, encompass aggressive types like glioblastoma and more indolent forms such as meningiomas and pituitary tumors, often leading to increased intracranial pressure, neurological dysfunction, and low survival rates despite multimodal treatment. Early and precise identification of tumor subtypes in MRI images remains challenging due to image noise, heterogeneity, and morphological variability, limiting real-time clinical diagnostics. To address these issues, we propose an improved YOLO11n model for brain tumor detection, incorporating lightweight GhostConv modules for reduced redundancy, Online Convolutional Reparameterization (OREPA) in the C3k2 module for enhanced efficiency, and Efficient Multi-scale Attention (EMA) for better multiscale feature capture. Using 4,000 annotated MRI images from a public Kaggle dataset (glioma, meningioma, pituitary tumor, and no tumor), divided into training, validation, and test sets (8:1:1 ratio), the model was trained over 200 epochs and evaluated on internal and external sets. The optimized model achieved a mean average precision (mAP@50) of 97.2% and recall of 93.8%, surpassing the baseline YOLO11n by 2.1% in mAP@50 while reducing GFLOPS by 25% from 6.4 to 4.8, demonstrating superior accuracy, efficiency, and lightweight design for edge deployment. This approach not only facilitates rapid tumor localization and classification in clinical practice but also supports personalized treatment planning, offering extensible solutions for broader medical imaging applications and improved patient outcomes.
Chen et al. (Fri,) studied this question.