## 1 Introduction  
Red blood cells (RBCs) play a crucial role in the transportation of oxygen within the blood. The health of RBCs is vital to their ability to efficiently flow through blood vessels, facilitated by their deformable membrane [1]. Elasticity is a key characteristic of healthy RBCs and is influenced by various natural factors, including the age of the cells. However, the impact of different diseases such as malaria [2], leukemia [3], diabetes [4], or sickle cell disease [5; 6; 7; 8] significantly affects RBC elasticity. Classifying RBCs can provide valuable insights and benefits in various scientific and medical contexts. Firstly, the accurate classification of RBCs enables the identification and characterization of different cellular types, such as normal RBCs, sickle cells, and other abnormal cell morphologies [9]. This classification can aid in the diagnosis and monitoring of various blood disorders, including sickle cell disease, thalassemia [10], and hereditary spherocytosis [11]. By distinguishing between different types of RBCs, healthcare professionals can better understand disease progression, assess the severity of conditions, and tailor treatment strategies accordingly. A more general overview of the techniques and importance of capturing cells of different elasticity can be found in [12; 13]. Furthermore, RBC classification can contribute to the understanding of physical and pathological processes within the human body. For instance, variations in RBC morphology may be indicative of certain underlying health conditions or physiological changes [14]. By analyzing and classifying RBCs, researchers can investigate the impact of factors such as nutrition, disease states, genetic variations, and environmental exposures on RBC characteristics. This knowledge can provide valuable insights into the mechanisms underlying various diseases and conditions, as well as facilitate the development of novel therapeutic approaches. Moreover, RBC classification has implications beyond clinical and research settings. Industries involved in blood transfusion services and blood banking heavily rely on accurate identification and classification of RBCs to ensure compatibility and safety in transfusion procedures. Classifying RBCs according to specific markers or characteristics allows for precise matching between donors and recipients, minimizing the risk of adverse transfusion reactions. This study aims to determine the elasticity of RBCs by analyzing the geometric characteristics captured in video recordings. Unfortunately, there is a scarcity of available blood flow recordings, and the limited number of videos is insufficient to train a neural network (NN) adequately, even with data augmentation techniques. One alternative approach is to employ computer simulations based on a numerical model that can be tailored to the experimental requirements, such as the desired blood flow properties and structure. Computer simulations offer a more detailed understanding of the behavior of the investigated blood flow compared to limited video recordings. Therefore, the integration of computer simulations in the study of hemodynamics and the development of new diagnostic techniques holds significant value. The simulation model used in this study is an extension of ESPResSo and incorporates the representation of blood cells as elastic objects within a fluid flow. The validity of the model is continuously assessed by comparing simulation results with laboratory experiments [15]. By utilizing simulations, a substantial amount of data for training NNs can be generated, particularly from video analyses of such experiments. The simulation model employed in our study controls the elasticity of RBCs using five parameters, which are described in detail in Section 3. The determination of RBC elasticity through simulation experiments has been investigated in prior works [16; 17]. However, this paper proposes a novel approach capable of identifying various levels of RBC elasticity associated with different diseases from video recordings.