雷-奥斯特里特复杂图形测试自动评分的一个基准
Juan Guerrero-Martín1, María Del Carmen Díaz-Mardomingo2, Sara García-Herranz3
1Department of Artificial Intelligence, UNED, Madrid, Spain.
Heliyon
|November 18, 2024
概括
这项研究引入了一个新的基准,用于自动评分雷-奥斯特里特复杂数字 (ROCF) 测试,帮助早期检测认知衰退. 一个草图优化的CNN取得了强的结果,为未来的研究奠定了基础.
科学领域:
- 神经科学是一个神经科学.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 雷-奥斯特里特复杂数字 (ROCF) 测试对于检测老年人的认知衰退至关重要.
- 通过计算机视觉自动化ROCF测试分析是具有挑战性的,因为缺乏标准化的基准.
- 现有的自动化系统缺乏公平的比较框架,阻碍了进步.
研究的目的:
- 为ROCF测试的自动评分建立一个基准测试框架.
- 介绍ROCFD528数据集,这是ROCF线图的第一个开放数据集.
- 为ROCF分析提供使用深度学习模型的基线实验结果.
主要方法:
- 开发了一个用于自动ROCF测试得分的基准测试框架.
- 创建了ROCFD528数据集,包括ROCF线图.
- 评估使用传统和转移学习的先进的卷积神经网络 (CNN).
主要成果:
- ROCFD528数据集是ROCF线图的第一个开放数据集.
- 设计用于草图的CNN在有限数据场景 (MAE = 3.448) 中优于其他CNN架构.
- 在ROCF分析上建立了深度学习模型的基线性能指标.
结论:
- 拟议的框架和数据集有助于公平地比较自动化ROCF评分系统.
- 草图优化的CNN显示出分析线图的前景,特别是在有限的数据下.
- 这项工作作为开发强大的机器学习工具的模型,用于草图和线图分析.
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