# Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity
## 1 Introduction  
The elasticity of red blood cells (RBCs), also referred to as erythrocytes, is one of the key physiological parameters that affect their functionality and ability to perform their primary tasks, such as transporting oxygen and carbon dioxide between the lungs and tissues. They must be able to deform and pass through narrow splenic slits with a width of 1-2 \(\upmu\)m. Erythrocytes that are unable to properly squeeze through these splenic slits become trapped and are removed from circulation [1; 2]. A loss of RBC deformability accompanies many pathological conditions, such as diabetes, sickle cell anemia, thalassemia, malaria, and others [3; 4; 5]. Thus, RBC deformability is justified as a physical biomarker of RBC dysfunction. Another area where RBC deformability is important is transfusion medicine, as the loss of RBC deformability is a consequence of RBC aging and storage lesions [6]. The deformability of RBCs is determined by a combination of the viscoelasticity of the plasma membrane, the viscosity of the cytoplasm, and cell geometry [7; 8; 9; 10]. These individual contributions are influenced by a wide range of endogenous and exogenous factors, including the composition and structure of the cell membrane, intracellular factors, physical and chemical environmental conditions, and various pathological states. A recent review study [11] provides a summary of the latest findings on these factors and highlights the need to analyze them in light of the new concept of cryptosis. In the last decade, machine learning has been increasingly used in the biomedical field to analyze complex biological data and improve diagnostic procedures. In the context of determining the elasticity of RBCs, machine learning brings new possibilities for theanalysis of large volumes of data, the identification of subtle patterns, and the prediction of the mechanical properties of RBCs. One of the main uses of machine learning in determining the elasticity of RBCs is the analysis of image data obtained from microscopic measurements. Convolutional neural networks (CNNs) find use in the automatic analyses of the microscopic images of RBCs or for the segmentation of RBCs from complex images, which are crucial for subsequent analysis [12; 13]. CNNs can identify and quantify the deformations of RBCs based on their shape and size, thus enabling the fast and accurate analysis of large datasets [14]. Machine learning methods are also used to predict the mechanical properties of RBCs based on various input data [15; 16]. Various regression or classification models can be trained to predict RBC elasticity based on physiological and biochemical parameters such as ion concentrations, membrane composition, and cytoskeleton state. Deep neural networks can process complex data inputs and create predictive models that take into account non-linear relationships between various biological factors and RBC elasticity. One of the great advantages of using machine learning is the automation of analyses. Machine learning algorithms can be integrated into diagnostic instruments, enabling the rapid and accurate analysis of RBC elasticity in a clinical setting. They can also effectively process and analyze big data, which is especially important for population studies and clinical research. Although machine learning methods offer significant benefits, there are also some limitations and challenges. The most significant limitations include data quality, model interpretability, and overfitting. The accuracy of the models depends on the quality and quantity of input data. Poor-quality or incomplete data can lead to erroneous predictions. Some models, especially deep neural networks, can be difficult to interpret, complicating the understanding of biological mechanisms. And, in the absence of large enough datasets, models can be overtrained, which leads to weaker generalization to new, independent data. Problems with data quality and range can be solved in standard ways (data augmentation, the identification and removal of outliers, etc.).