基于改进YOLOv8的脑肿瘤图像检测算法OA

Brain tumors are a severe threat to human health,and early detection is crucial for improving treatment out-comes.To address the issue of insufficient detection accuracy in existing brain tumor detection algorithms under complex backgrounds or with blurred tumor boundaries,this paper proposes an improved YOLOv8-based brain tumor detection algo-rithm.The algorithm introduces a coordinate attention mechanism to enhance feature focus on tumor regions;employs depthwise separable convolution in the backbone network to reduce computational complexity and improve feature extraction efficiency;and incorporates the bottleneck transformer module to strengthen global information modeling.Experiments were conducted on the brain tumor detection dataset,and the results show that the detection accuracy reached 93％,an improve-ment of 1.1％over the original algorithm.mAP0.5 and mAP0.5∶0.95 were increased by 2.1％and 1.7％,respectively.The experimental results demonstrate that the improved algorithm exhibits significant advantages in brain tumor detection tasks,providing more accurate and efficient support for medical image-assisted diagnosis.