KWC-YOLO: An efficient YOLO architecture for lumbar spinal stenosis grading through dynamic convolution and...

Lumbar spinal stenosis (LSS) represents a significant global health burden, and its diagnosis from Magnetic Resonance Imaging (MRI) is often subject to inter-observer variability and time-consuming interpretation. While deep learning (DL) models offer a promising solution, they are frequently constrained by the scarcity of annotated medical data, high computational demands, and challenges in representing subtle pathological features. To address these limitations, we propose KWC-YOLO, a novel and efficient object detection framework for the automated detection and classification of lumbar central canal stenosis (LCCS) severity according to the Schizas grading criteria. Our model enhances the YOLOv11n architecture through three core innovations: (1) the integration of KernelWarehouse (KWConv), a parameter-efficient dynamic convolution mechanism that improves the feature adaptability of the detection head; (2) the introduction of a FasterGATE activation unit in the backbone to enhance non-linear representation and accelerate convergence; and (3) the implementation of a lightweight Slim-Neck structure, which optimizes the trade-off between feature fusion quality and computational cost. On a clinical lumbar spine MRI dataset, KWC-YOLO demonstrates superior performance, achieving a mean Average Precision at an IoU of 0.5 (AP₅₀) of 86.7% and an AP₉₅ of 63.0%. This represents a substantial improvement over the YOLOv11n baseline by 9.2 and 9.3 percentage points in AP₅₀ and AP₉₅ respectively, while simultaneously reducing the computational load by 36.5% to 4.0 GFLOPs. Conclusively, KWC-YOLO establishes a new benchmark for automated LCCS grading. Its compelling balance of high accuracy and computational efficiency holds the potential to alleviate the interpretative burden on radiologists, enhance reporting accuracy, and streamline clinical decision-making, ultimately leading to improved patient outcomes.