Scholarlink

<p>For MRI brain tumor image segmentation, it is necessary to have high real-time and accurate edge localization. Therefore, we propose a yolov8n based on dynamic serpentine convolution and multi-feature attention method (DMA-YOLOV8n). The method combines dynamic serpentine convolution and multi-feature attention mechanism, which can effectively adapt to different brain tumor tissue edge morphology changes and more accurately segmented to obtain brain tumor and locate its edge position. First, dynamic serpentine convolution is used to replace standard convolution in C2f. module. Then, drawing on the idea of skip connection in U-Net model, multi- feature fusion is used to connect multilayer sampling information to retain more feature details and improve edge segmentation accuracy. Finally, dual attention mechanism is added to multilayer feature fusion to pay more attention to brain tumor tissue. DMA-YOLOV8n is applied to brain MRI images from Kaggle_3M dataset. Experimental results show the method has mAP50: 0.806 and mAP50:95: 0.490.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Yolov8n based on dynamic serpentine convolution and multi-feature attention for MRI brain cranial tumor segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月4日

查看全文
  • Yiliu Hang,
  • Qiong Zhang,
  • Li Li,
  • Chunhua Lin

摘要

For MRI brain tumor image segmentation, it is necessary to have high real-time and accurate edge localization. Therefore, we propose a yolov8n based on dynamic serpentine convolution and multi-feature attention method (DMA-YOLOV8n). The method combines dynamic serpentine convolution and multi-feature attention mechanism, which can effectively adapt to different brain tumor tissue edge morphology changes and more accurately segmented to obtain brain tumor and locate its edge position. First, dynamic serpentine convolution is used to replace standard convolution in C2f. module. Then, drawing on the idea of skip connection in U-Net model, multi- feature fusion is used to connect multilayer sampling information to retain more feature details and improve edge segmentation accuracy. Finally, dual attention mechanism is added to multilayer feature fusion to pay more attention to brain tumor tissue. DMA-YOLOV8n is applied to brain MRI images from Kaggle_3M dataset. Experimental results show the method has mAP50: 0.806 and mAP50:95: 0.490.