Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, 08901, USA.
Biomed Microdevices. 2024 May 23;26(2):25. doi: 10.1007/s10544-024-00707-0.
Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires accurate and efficient analysis of particle properties. In this study, we investigated the integration of electrical and optical features through a multimodal approach for particle classification. Machine learning classifier algorithms were applied to evaluate the impact of combining these measurements. Our results demonstrate the superiority of the multimodal approach over analyzing electrical or optical features independently. We achieved an average test accuracy of 94.9% by integrating both modalities, compared to 66.4% for electrical features alone and 90.7% for optical features alone. This highlights the complementary nature of electrical and optical information and its potential for enhancing classification performance. By leveraging electrical sensing and optical imaging techniques, our multimodal approach provides deeper insights into particle properties and offers a more comprehensive understanding of complex biological systems.
粒子分类在各种科学和技术应用中起着至关重要的作用，例如在医疗保健应用中区分细菌和病毒，或识别和分类癌细胞。这项技术需要对粒子特性进行准确高效的分析。在这项研究中，我们通过多模态方法研究了电和光特性的集成，以用于粒子分类。应用机器学习分类器算法来评估组合这些测量的效果。我们的结果表明，与单独分析电或光特征相比，多模态方法具有优越性。通过整合两种模式，我们实现了平均测试准确率为 94.9%，而仅使用电特征的准确率为 66.4%，仅使用光特征的准确率为 90.7%。这突出了电和光信息的互补性及其增强分类性能的潜力。通过利用电传感和光学成像技术，我们的多模态方法提供了对粒子特性的更深入了解，并对复杂生物系统有了更全面的认识。
