Abstract
ObjectiveTo address challenges such as blurred boundaries and irregular shapes in brain tumor magnetic resonance imaging scans, we developed a lightweight detection framework to enhance automated diagnosis and meet real-time clinical requirements.MethodsWe proposed an improved You Only Look Once version 12 (YOLOv12n)-based model featuring three modules. First, the Attention-based C2f with Frequency-domain Feed-Forward Network (A2C2f-DFFN) module was incorporated into the backbone network; it combined an attention mechanism with a frequency-domain feedforward network to enhance global context modeling and detailed feature reconstruction. Second, the C2f with Token Statistics Self-Attention and Dynamic Tanh (C2TSSA-DYT) module was employed in the feature fusion neck; it utilized statistical self-attention and a dynamic Tanh activation function to improve robustness in complex backgrounds. Finally, the dynamic upsampling operator was adopted in the feature reconstruction stage; it dynamically generated sampling weights to effectively prevent boundary blurring and detail loss.ResultsOn the Kaggle brain tumor dataset, our method achieved 93.2% precision, 88.4% recall, and 94.1% mean average precision at IoU threshold 0.5 (mAP@0.5), surpassing YOLOv12n and other lightweight models. It showed excellent performance in patients with glioma and pituitary tumor cases using only 6.0 Giga Floating-point Operations Per Second (GFLOPs) and 2.76 M parameters for efficient real-time inference.ConclusionThe enhanced YOLOv12n framework proposed in this study achieved good balance between accuracy and efficiency in brain tumor detection tasks, demonstrating strong robustness, which makes it suitable for use in clinical computer-aided diagnosis systems.