Artificial Neural Networks for GMR-Based Magnetic Cytometry

In this work, we propose an artificial neural network (ANN) for magnetic microcytometry pattern recognition and automated counting. The method is tested for detecting analytes in the 2-3-mu m range. The cytometer is composed of a disposable cartridge and an acquisition platform. The disposable cartridge contains microfluidic channels with 10 x 100 mu m2 cross section on top of a substrate with magnetoresistive (MR) sensors. The custom analog signal chain performs with an integrated noise of 2.99 mu Vrms in a 10-kHz bandwidth. To employ the ANN, we synthesize a training dataset based on the magnetic-dipole equation and several dataset expansion methods. The ANN is tested on an experiment with 2.8-mu m magnetic particles (MPs) and compared with an improved threshold-based method with reduced false positives. The ANN produces a maximum of 90% detection rate, improving on the 30%-50% detection rates of other single-sensor methods published in the literature.

Automated summary

In this study, an artificial neural network (ANN) is proposed for magnetic microcytometry pattern recognition and automated counting. The method is tested for detecting analytes in the range of 2-3 μm. The cytometer consists of a disposable cartridge with microfluidic channels and magnetoresistive (MR) sensors. The ANN achieves a maximum detection rate of 90%, surpassing the detection rates of other single-sensor methods reported in the literature (30%-50%).