使用YOLOv8和ResNet,SeResNet和视觉变压器 (ViT) 算法的综合方法,基于对肘部X射线图像中的ROI骨折预测
Taukir Alam1, Wei-Cheng Yeh1,2, Fang Rong Hsu1
1Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan.
Current medical imaging
|October 3, 2024
概括
这项研究使用人工智能算法提高了对X射线图像的肘部骨折预测,如YOLOv8,ResNet,SeResNet和Vision Transformer. 人工智能模型实现了高精度,提高了诊断精度.
科学领域:
- 医疗成像医学成像
- 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:
- 肘部骨折需要准确和及时的诊断.
- 目前的诊断方法可能会耗时,可能会受益于人工智能的协助.
研究的目的:
- 在X射线图像上使用先进的AI算法来完善肘部骨折预测.
- 为了比较YOLOv8,ResNet,SeResNet和Vision Transformer在断裂检测方面的性能.
主要方法:
- 在肘部X射线中利用YOLOv8初步识别感兴趣区域 (ROI).
- 集成并比较ResNet,SeResNet和视觉变压器算法用于骨折预测.
- 应用图像增强技术和重新校准的ROI,以提高诊断准确度.
主要成果:
- 视觉变压器 (ViT) 获得了最高的精度 (0.99),ResNet和SeResNet获得了0.97.
- 所有算法都显示出高精度 (1.0) 和回忆 (0.95).
- 综合方法显著提高了骨折预测的准确性.
结论:
- 开发的人工智能驱动方法显示了在肘部骨折检测中提高诊断精度的潜力.
- 这项技术可以帮助放射科医生,集成到现有系统中,并支持临床决策,以改善患者护理.
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