安徽大学学报（自然科学版）2026，Vol.50Issue(3)：53-59,7.DOI:10.3969/j.issn.1000-2162.2026.03.008
基于改进YOLOv11的CT图像中的脑膜瘤检测
Meningioma detection in CT images based on improved YOLOv11
摘要
Abstract
To address challenges such as inconsistent shapes and sizes,as well as complex backgrounds in meningioma detection tasks,this paper proposed a meningioma detection algorithm for CT images based on an improved YOLOv11(You Only Look Once version 11).First,designing a multi-dimensional attention fusion mechanism and introducing a depthwise separable convolution optimize the network architecture.Second,using DIoU as the bounding box loss function accelerates model convergence.Finally,the effectiveness of the proposed method was validated through specific experiments and compared with other algorithms.The results indicate that compared with the other five algorithms,the proposed algorithm achieved the highest mean values for accuracy,recall,and average precision,reaching 96.2％,99.1％,and 98.8％,respectively.关键词
脑膜瘤检测/YOLOv11/深度可分离卷积/注意力机制/损失函数Key words
meningioma detection/YOLOv11/depthwise separable convolution/attention mechanism/loss function分类
信息技术与安全科学引用本文复制引用
江永成,刘元志,胡根生,魏子靖..基于改进YOLOv11的CT图像中的脑膜瘤检测[J].安徽大学学报（自然科学版）,2026,50(3):53-59,7.基金项目
国家自然科学基金资助项目(32372632,52175210) （32372632,52175210）
