YOLO-LS: A Novel Deep Learning Framework for Brain Tumor Segmentation in Magnetic Resonance Imaging

Brain tumors, as highly heterogeneous intracranial diseases, require accurate recognition and segmentation for clinical diagnosis, surgical planning, and prognosis evaluation. However, manual annotation of traditional MRI images suffers from subjective bias and low efficiency. This study proposes an improved algorithm, YOLO-LS, based on the YOLO11-Seg model to enhance real-time recognition, detection, and high-precision segmentation of brain tumor MRI images. Specific improvements include the adoption of ShuffleNet V1 as a lightweight backbone network to reduce parameters and computational complexity; the introduction of the DySample dynamic upsampling mechanism to enhance detail recovery; and the optimization of the C3k2 module into a C3k2-PoolingFormer block for efficient cross-scale feature fusion. Experiments were conducted using the Figshare dataset (3064 images) for training and internal testing, with the Kaggle dataset (300 images) serving as external validation. Results indicate that YOLO-LS achieved a bounding box mAP50 of 0.953 and a Dice coefficient of 0.910 on the internal test set, with GFLOPs reduced to 8.1, representing a 2.9% precision improvement and a 15.6% reduction in computational load compared to the baseline YOLO11. Ablation experiments and comparisons with models such as U-Net and SegNet confirmed the effectiveness of the improvements; heatmaps further validated the model's precise focus on tumor boundaries. On the external test set, the model demonstrated strong generalization, with an overall Dice coefficient of 0.895. This method achieves an excellent balance among precision, efficiency, and interpretability, showing significant potential for clinical applications and future extensions to multimodal fusion and federated learning.