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Machine learning-based screening of red blood cells using quantitative phase imaging with micro-spectrocolorimetry
Optics & Laser Technology ( IF 5 ) Pub Date : 2019-12-10 , DOI: 10.1016/j.optlastec.2019.105980
Veena Singh , Vishal Srivastava , Dalip S. Mehta

We report simultaneous micro-spectrocolorimetry and quantitative phase imaging (QPI) of human red blood cells (RBCs) using white light interference microscopy. To understand the mechanism of disease at the cellular level and to increase the diagnostic performance of QPI, we integrated it with micro-spectrocolorimetry. Color-coordinates of various spatial locations of RBCs along with 3D phase-maps help to quantify different biophysical parameters. Features extracted from this multimodal technique combined with support vector machine achieved average specificity, sensitivity, and accuracy of 95.52%, 95.58%, and 95.55%, respectively with testing data in classification of healthy and unhealthy RBCs. A better result is obtained by using synergies amongst data as compared to QPI based features only.



中文翻译:

基于机器学习的红细胞定量相成像与微分光比色法筛查

我们报告同时使用白光干涉显微镜的人类红细胞(RBCs)的显微光谱比色法和定量相成像(QPI)。为了了解细胞水平上的疾病机理并提高QPI的诊断性能,我们将其与微分光比色法集成在一起。RBC的各种空间位置的颜色坐标以及3D相图有助于量化不同的生物物理参数。从这种多峰技术中提取的特征与支持向量机相结合,通过对健康和不健康RBC进行分类的测试数据,平均特异性,灵敏度和准确度分别达到95.52%,95.58%和95.55%。与仅基于QPI的功能相比,通过使用数据之间的协同作用可获得更好的结果。

更新日期:2019-12-10
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