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淋巴结节症分类中的深度学习:对模型架构和单模与多模方法的比较研究
Sahika Betul Yayli1, Kutay Kılıç1, Salih Beyaz2
1Artificial Intelligence and Digital Analytics Solutions, Turkcell Technology, Istanbul, Türkiye.
Frontiers in artificial intelligence
|February 20, 2025
概括
单模型深度学习方法在分类凯尔格伦-劳伦斯骨关节炎阶段方面比多模型策略更有效. 这项研究强调了选择特定任务的卷积神经网络架构对于准确的KL分级的重要性.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 骨关节炎 (OA) 诊断依赖于放射性评估,通常使用Kellgren-Lawrence (KL) 评分系统.
- 准确的KL分期对于治疗规划和预后至关重要.
- 深度学习 (DL) 提供了从X射线图像自动化OA评估的潜力.
研究的目的:
- 为了比较单模型和多模型DL方法对分类KL OA阶段的有效性.
- 为了评估七个不同的卷积神经网络 (CNN) 架构的性能.
- 评估对比限度自适应基因图平衡 (CLAHE) 对分类准确性的影响.
主要方法:
- 一套14607张膝关节前后部 (AP) X射线的数据集被策划和注释.
- 对象检测 (YOLOv5) 用于膝关节关节区域的隔离.
- 一个单模型DL方法和一个多模型DL方法 (骨菌检测,关节空间缩小,联合分类) 被实施和比较.
- 七个CNN架构被训练有或没有CLAHE增强.
主要成果:
- 与多模型方法相比,单模型方法获得了更高的性能 (F1得分:0.763,准确率:0.767) (F1得分:0.736,准确率:0.740).
- 在不同的CNN架构和任务中,性能差异很大,这表明需要对架构进行优化.
- 一般来说,CLAHE增强对分类性能产生了负面影响,在一个实例中改善的程度很小.
结论:
- 单一模型DL方法证明了对膝盖OA的KL分级的卓越有效性.
- 针对特定任务的CNN架构选择和适当的预处理对于最佳性能至关重要.
- 未来的研究应该专注于组合方法,先进的增强技术和临床验证.
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