A High Performance of Single Cell Imaging Detection with Deep Learning

Single cell imaging enables new applications such as biomedical diagnostics, food inspection, and water quality monitoring. In this paper, we study the cell imaging-based machine learning techniques for high-performance cell detection. By taking the advantage of deep learning and imaging flow cytometry, we manage to detect Cryptosporidium and Giardia cells in the bright-field images with high accuracy and high speed on embedded GPU system. Our experiments demonstrate that the newly developed deep learning-based algorithms surpasses the hand-crafted features and SVM-based algorithms. We achieved above 99 percentage in accuracy and 580+fps in speed on embedded Jetson TX2 platform. Our research will lead to a highly accurate real-time single cell level detection system in future.