基于机器和深度学习的生物医学拉曼光谱 (RS) 分类算法的比较研究:基于RS的病原微生物识别案例研究
Sisi Guo1, Ruoyu Zhang2, Tao Wang3
1Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
概括
这项研究将机器学习 (ML) 和深度学习 (DL) 进行了比较,用于使用拉曼光谱识别微生物. DL在完整的数据中表现出色,而ML在有限的光谱中表现更好,显示了算法依赖数据量.
科学领域:
- 生物医学光谱学
- 机器学习在诊断中的应用.
- 深度学习用于微生物识别.
背景情况:
- 生物医学拉曼光谱 (RS) 的进步是由机器学习 (ML) 和深度学习 (DL) 算法驱动的.
- 由于有限的开源光谱数据,生物医学RS的ML和DL缺乏系统的比较.
研究的目的:
- 为了比较典型的ML算法 (PLS-DA) 和DL算法 (1D-CNN) 的性能,用于使用拉曼光谱识别致病微生物.
- 在不同大小的数据集中评估算法性能 (100%,75%,50%,25%的12,000个光谱).
主要方法:
- 利用了6种微生物物种的12,000个拉曼光谱.
- 对比部分最小方形差异分析 (PLS-DA) 和一维卷积神经网络 (1D-CNN).
- 雇佣了一个80%的培训和20%的测试数据分为分析.
主要成果:
- 在100%的数据中,1D-CNN获得了比PLS-DA更高的准确性 (95.25%) 和AUC (0.997) (89.42%的准确性,0.979 AUC).
- 在仅使用75%,50%和25%的拉曼光谱时,PLS-DA的性能优于1D-CNN.
- 这两种方法都证明了对光谱数量的依赖,并显示了关键光谱特征 (DNA,蛋白质) 的可比解释性.
结论:
- 无论是ML还是DL算法都对拉曼光谱识别有价值.
- 选择算法应该取决于应用程序,考虑数据的可用性和所需的准确性.
- 建议对ML和DL进行进一步的探索,以优化通过拉曼光谱的微生物识别.
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