YOLO-LS: a novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging.

Brain tumors exhibit high heterogeneity in morphology, texture, and location, making accurate recognition and segmentation critical for clinical diagnosis, surgical planning, and prognosis evaluation. However, manual annotation of MRI scans is hindered by subjective bias and inefficiency. Furthermore, existing automated frameworks often face a trade-off between segmentation precision-particularly at infiltrative boundaries-and the computational efficiency required for deployment in resource-constrained environments. To address these challenges, this study proposes YOLO-LS (Lightweight Segmentation), an enhanced framework based on the YOLO11n-seg architecture designed for efficient detection and high-precision segmentation of brain tumors. The methodology introduces three key innovations: (1) integrating ShuffleNet V1 as a lightweight backbone to significantly reduce parameter count and computational complexity via pointwise grouped convolutions; (2) incorporating the DySample dynamic upsampling mechanism to mitigate the loss of fine-grained semantic details inherent in traditional interpolation, thereby improving the recovery of tumor boundaries; and (3) optimizing the neck network with a C3k2-PoolingFormer module to facilitate efficient cross-scale feature fusion and global context capture. The model was trained and tested on the Figshare dataset (3,064 images) using five-fold cross-validation and externally validated on an independent Kaggle dataset (300 images). Results demonstrate that YOLO-LS achieved a bounding box mAP50 of 0.953 ± 0.011, a Dice coefficient of 0.91 ± 0.01, and a 95% Hausdorff Distance (HD