深度学习模型用于自动识别和分类距离半径断裂
Kaifeng Gan1, Yunpeng Liu2, Ting Zhang1
1Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China.
Journal of imaging informatics in medicine
|June 11, 2024
概括
这项研究开发了深度学习模型,用于自动检测和分类手腕骨折从X射线图. DenseNet121模型在识别和分类远半径骨折 (DRF) 中取得了高准确性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 整形外科 整形外科 整形外科
背景情况:
- 距离半径骨折 (DRF) 是一个常见的手腕损伤.
- 准确和及时的诊断对于有效的治疗至关重要.
- 当前的诊断方法可能是耗时的和主观的.
研究的目的:
- 开发和评估深度学习模型,用于自动细分,识别和分类DRF从手腕放射图.
- 评估各种深度学习架构在分析手腕X射线中的性能.
主要方法:
- 使用了2240张前后手腕X射线图的数据集.
- 雇佣了Unet和Fast-RCNN用于自动X线图细分.
- 应用了DenseNet121和ResNet50用于DRF识别.
- 使用了DenseNet121,ResNet50,VGG-19和InceptionV3进行DRF分类 (A,B,C类型).
- 评估模型使用曲线下的面积 (AUC),准确性,精度和F1分数.
主要成果:
- 深度学习模型展示了手腕放射图的有效细分.
- 对于DRF识别,DenseNet121获得了0.941的AUC.
- 对于分类A,B和C类的DRF,DenseNet121的AUC值为0.96.
- 在2240名参与者中,有1440人患有DRF,不同类型.
结论:
- 深度学习模型显示了手腕放射图的自动化分析的巨大潜力.
- DenseNet121模型是帮助临床医生诊断和分类DRF的一个有希望的工具.
- 自动化分析可以提高解释手腕骨折的效率和准确性.
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