Supervised Machine Learning for Cell Classification in Microwave Flow Cytometers

Microwave sensing of biological cells is of great interest for its noncontact, noninvasive nature and high-sensitivity, high-resolution potential. Nevertheless, the lack of measurement specificity has been a critical problem. In this work, we show that highly reproducible, unbiased microwave data of single cells, analyzed by classical machine learning (ML) algorithms, enable effective cell classification. Yeast cells, including Saccharomyces cerevisiae, Saccharomyces pastorianus, Candida albicans, Candida tropicalis, Candida parapsilosis, and Candida krusei, were measured with microwave flow cytometers at frequencies between 0.265 and 7.65 GHz. Four sets of yeast cell data with 7941, 310, 130, and 200 cells were obtained. ML models, including random forest, XGBoost, K-neighbors, decision tree, and support vector machine (SVM), were trained to classify cell types and viability. Accuracies of up to 86.3% were obtained for cell type classification. The results show that the addition of computer vision features via cell imaging helps improve accuracy and that ML model performance depends on its targeted cell traits, the number of frequencies applied, and the values of chosen frequencies. Standardized data acquisition and significantly more cell data are needed for further ML classification investigation.