凭借速度和精度之间的良好平衡,YOLO（You Only Look Once）框架已成为目标检测最有效的算法之一。在医学脑肿瘤检测领域,基于YOLOv8算法提出了一种改进后的创新算法YOLOv8-BBS。它将双层路由注意力机制（BRA,Bi-Level Routing Attention）与双模态注意力机制（BiFormer）两种注意力机制,构建成双层特征融合框架,巧妙融合在YOLOv8的骨干网络中,实现全面捕捉图像细节与全局特征;为了解决数据集类别不平衡及样本识别难的问题,本算法引入了滑动损失函数SlideLoss,实现类别权重的动态调整,增强数据分类的准确性与鲁棒性。在脑肿瘤MRI数据集上的实验结果表明,本文提出的模型在速度和准确性方面优于YOLOv5、YOLOv8、YOLOv9和YOLOv10,YOLOv8-BBS的F1分数相较于YOLOv8提高了1%,准确率达到了81.8%,召回率为72.7%,证实了该模型在脑肿瘤检测中的有效性,为后续的医学成像和临床疾病诊断中的物体检测应用提供了新的思路。

With a good balance between speed and accuracy, YOLO (You Only Look Once) framework has become one of the most effective algorithms for object detection. In the field of medical brain tumor detection, this paper proposes an improved and innovative algorithm, YOLOv8 - BBS, based on the YOLOv8 algorithm. It constructs a two - layer feature fusion framework by combining two attention mechanisms, namely the Bi - Level Routing Attention (BRA) and the Bi - modal Attention mechanism (BiFormer), and skillfully integrates this framework into the backbone network of YOLOv8. This enables the algorithm to comprehensively capture image details and global features. To address the problems of class imbalance in the dataset and the identification of difficult samples, this algorithm introduces the SlideLoss function, which dynamically adjusts the class weights, enhancing the accuracy and robustness of data classification. Experimental results on the brain tumor MRI dataset show that the model proposed in this paper outperforms YOLOv5, YOLOv8, YOLOv9, and YOLOv10 in terms of both speed and accuracy. The F1 - score of YOLOv8 - BBS is 1% higher than that of YOLOv8, with an accuracy rate of 81.8% and a recall rate of 72.7%. These improvements confirm the effectiveness of the model in brain tumor detection and provide new ideas for subsequent object detection applications in medical imaging and clinical disease diagnosis.