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基于ResNet的骨干集成YOLO框架用于骨折检测
Diptendu Bhattacharya1, Subhradip Das1, Tamal Biswas2
1Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India.
一种新的混合 ResYOLO11 模型使用人工智能增强了骨折检测. 这种人工智能工具为骨科专家提供了更高的准确性和速度,有助于医学诊断中的临床决策.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 准确的骨折检测对于有效的患者治疗至关重要.
- 人工智能 (AI) 和机器学习 (ML) 在医学诊断中越来越重要.
- 现有的模型需要优化,以精确地定位断裂.
研究的目的:
- 引入一种新的混合 ResYOLO11 架构,用于增强骨折检测.
- 将ResNet50的特征提取强度与YOLO11.11的检测效率相结合.
- 改善空间特征表示,分类精度和医疗成像中的检测稳定性.
主要方法:
- 开发了一个混合ResYOLO11架构,将ResNet50骨干与YOLO11模块 (C3K2,SPPF,C2PSA) 集成在一起.
- 在公共GRAZPEDWRI-DX数据集上训练和评估模型.
- 使用精度,回忆,mAP@50和mAP@50-95作为关键性能指标.
主要成果:
- 在各种模型尺寸中,ResYOLO11实现了高精度得分 (0.935-0.963) 和mAP@50得分 (0.970-0.986).
- 与标准YOLO11变种相比,显示高达4.2%的mAP50和6.1%的mAP50-95.
- 实现了24%更快的断裂检测推断时间.
结论:
- ResYOLO11架构为人工智能驱动的骨折诊断提供了一个临床稳健和计算效率高的解决方案.
- 这种人工智能模型显示出在帮助骨科专家准确识别骨折方面有很大的潜力.
- 这些发现支持计算机辅助诊断在临床决策中的进步.
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