使用放射图像对脊柱曲率类型的分类:深度学习与经典方法对比
Parisa Tavana1, Mahdi Akraminia2, Abbas Koochari1
1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Artificial intelligence review
|June 26, 2023
概括
这项研究开发了一种自动化系统,用于分类脊柱脊柱曲的类型 (C形或S形). 像Xception和MobileNetV2这样的深度学习模型与SVM集成实现了比传统方法更高的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 脊柱形分析 脊柱形分析
背景情况:
- 脊椎形呈现C形或S形脊柱曲线,由于脊椎平衡时间,观察者偏差和图像质量,难以诊断.
- 精确的脊柱曲率类型的分类对于有效的脊柱脊柱病管理至关重要.
研究的目的:
- 通过自动分类脊柱曲率类型来评估脊柱形.
- 为了比较经典机器学习算法与深度学习模型的性能,用于脊柱曲率分类.
主要方法:
- 使用了支持向量机 (SVM) 和K-Nearest Neighbors (KNN) 算法,具有低级 (纹理) 和基于本地补丁 (字袋) 的功能.
- 采用预先训练的深度网络 (Xception,MobileNetV2),使用SVM作为自动化特征提取的最终激活功能.
- 在使用转移学习的私人数据集上训练了1000张前后脊椎放射图像的模型.
主要成果:
- 与经典方法相比,预训练的深度网络在分类C形与S形脊柱曲方面显示出大约10%的准确性.
- 通过深度网络进行自动化特征提取,特别是Xception和MobileNetV2与SVM,超过了与SVM/KNN结合的传统特征提取技术.
结论:
- 预先训练有素的深度学习网络与SVM集成提供了更准确和自动化的方法来分类脊柱脊柱曲的类型.
- 这种人工智能驱动的方法增强了脊柱形的诊断能力,有可能克服手动评估和传统机器学习的局限性.
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