RBC (Red Blood Cell) membrane is a highly elastic structure, and proper modelling of this elasticity is essential for biomedical applications that involve computational experiments with blood flow. In this work, we present a new method for estimating one of the key parameters of red blood cell elasticity, which uses a neural network trained on the simulation outputs. We test classic LSTM (Long-Short Term Memory) architecture for the time series regression task, and we also experiment with novel CNN-LSTM (Convolutional Neural Network) architecture. We paid special attention to investigating the impact of the way the three-dimensional training data are reduced to their two-dimensional projections. Such a comparison is possible thanks to working with simulation outputs that are equivalently defined for all dimensions and their combinations. The obtained results can be used as recommendations for an appropriate way to record real experiments for which the reduced dimension of the acquired data is essential.

The elasticity of red blood cells (RBCs) plays a vital role in their efficient movement through blood vessels, facilitating the transportation of oxygen within the bloodstream. However, various diseases significantly impact RBC elasticity, making it an important parameter for diagnosing and monitoring health conditions. In this study, we propose a novel approach to determine RBC elasticity by analyzing video recordings and using a convolutional neural network (CNN) for classification. Due to the scarcity of available blood flow recordings, computer simulations based on a numerical model are employed to generate a substantial amount of training data. The simulation model incorporates the representation of RBCs as elastic objects within a fluid flow, allowing for a detailed understanding of their behavior. We compare the performance of different CNN architectures, including ResNet and EfficientNet, for video classification of RBC elasticity. Our results demonstrate the potential of using CNNs and simulation-based data for the accurate classification of RBC elasticity.