An Intelligent Machine Learning-Based Sheath-free Microfluidic Impedance Flow cytometer

Abstract

This paper combines microfluidic impedance flow cytometers with Machine Learning (ML) to introduce a new set of sheaths free microchannels that can estimate the size and number of multi particles passing through the channel. A new definition named "Basis Impedances" also is introduced for reconstructing output impedance multiparticle systems in the proposed ML algorithm, and finally, we will show that the ML model can play the role of a fast and accurate surrogate model of the finite element-based simulation programs. The broader impact of the proposed method would be trying to find the Intelligent ways in order to fabricate low-cost, easy to use and highly accessible POCT-based lab-on-a-chip devices and it is the topic of our future works.