Development of a cell counting system based on machine learning

Abstract

Endothelial cells of the aorta are an excellent model system for studying inflammatory responses, angiogenesis, atherosclerosis, blood clotting, vascular contraction, and vasodilation. Cell counting is necessary when culturing human aortic endothelial cells (HAECs) in order to determine cell concentration and quantity, as well as to assess cell viability and proliferation. Here, a machine learning-based image recognition system was developed to create a cell counter. The system’s hardware includes a digital microscope, Raspberry Pi, and operating screen. Image capture, image processing, machine learning, and computer recognition are utilized in the processing techniques. The processing steps consist of training and testing stages. In the training stage, five HAECs images were selected as training samples while the remaining HAECs images were used as testing samples. Positive and negative samples were labeled using LabelImg and used to generate training images for the classifier program. The classifier program was trained using the built-in Adaboost and LBP models in Opencv to create a HAECs classifier. The system achieved a recognition rate of 95% for HAECs and 98% for colon cells in practical tests, demonstrating that this technology can be used as a tool for cell counting and can replace expensive and potentially inaccurate commercial cell counting software, making cell counting a more practical technique.