双向YOLOv10与平均卷积用于MRI中脑瘤检测
Yan Guo1, Xiaoxiang Huang1, Hua Chen2
1School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China.
Brain research bulletin
|February 15, 2026
概括
这项研究使用新的模块增强了YOLOv10用于脑瘤检测,提高了MRI扫描的准确性. 这种先进的模型为医疗专业人员提供了更可靠的自动诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 医疗专业人员使用MRI扫描来评估脑瘤,面临疲劳和主观解释的挑战.
- 自动检测系统对于提高诊断准确性和效率至关重要.
研究的目的:
- 增强YOLOv10对象检测模型,以提高脑瘤检测准确度.
- 介绍和评估AvConv模块和双向路径的有效性.
主要方法:
- 将AvConv模块和双向路径集成到YOLOv10架构中.
- 实验对两个Kaggle数据集 (1116张和153张图像) 进行了实验,对脑瘤进行了注释.
- 增强模型与八种主流物体检测方法的比较.
主要成果:
- AvConv模块使YOLOv10的mAP50提高了0.3% (88.5%) 和mAP50-95提高了3.1% (77.7%).
- 双向检测模型实现了90.3%的mAP50和83.5%的mAP50-95,超过了基线YOLOv10.
- 拟议的模块在检测准确度方面取得了显著的收益,并保持了竞争力的推断效率.
结论:
- 集成的AvConv模块和双向路径有效地增强YOLOv10用于脑瘤检测.
- 定制深度学习架构显示出可靠,自动化脑瘤诊断的前景.
- 该研究支持开发先进的人工智能工具,以帮助医疗专业人员解释MRI扫描.
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