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在使用深度学习方法的放射图像中诊断和检测骨折
Theyazn Aldhyani1, Zeyad A T Ahmed2, Bayan M Alsharbi3
1Applied College, King Faisal University, Al-Ahsa, Saudi Arabia.
Frontiers in medicine
|February 10, 2025
概括
深度学习模型使用X射线图像准确地检测骨折. 这种自动化方法实现了97%的准确性,改善了骨折诊断和放射系统.
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:
- 骨折是常见的伤害,需要准确的诊断.
- 传统的骨折检测方法面临着挑战.
- 深度学习 (DL) 为医学图像分析提供了先进的解决方案.
研究的目的:
- 利用深度学习开发一个用于骨折检测的自动化系统.
- 在多区域X射线数据集上评估DL模型 (VGG16,ResNet152V2,DenseNet201) 的有效性.
- 为了提高骨折诊断的准确性和效率.
主要方法:
- 使用了Kaggle多区域X射线数据集,包括10580张图像.
- 实施并比较了深度学习模型:VGG16,ResNet152V2和DenseNet201.
- 专注于自动骨折检测和分类.
主要成果:
- 拟议的DL系统在验证过程中实现了97%的准确率.
- 结合了DenseNet201和VGG16,表现出了很高的性能.
- 该系统准确地识别和分类了各种骨折类型.
结论:
- 深度学习模型显著提高了骨折检测准确度.
- 开发的系统为改进的放射性诊断工具提供了基础.
- 自动化DL方法对于推进骨科诊断至关重要.
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