## 3 Data Preparation  
The source data used to classify the health of RBCs were obtained from multiple simulation experiments. These experiments were conducted using the open-source software ESPResSo [25], incorporating its lattice-Boltzmann and object-in-fluid modules [26]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Operator** & **Resolution** & **Channels** & **Layers** \\ \hline
1 & Conv3x3 & \(224\times 224\) & 32 & 1 \\
2 & MBConv1, k3x3 & \(112\times 112\) & 16 & 1 \\
3 & MBConv6, k3x3 & \(112\times 112\) & 24 & 2 \\
4 & MBConv6, k5x5 & \(56\times 56\) & 40 & 2 \\
5 & MBConv6, k3x3 & \(28\times 28\) & 80 & 3 \\
6 & MBConv6, k5x5 & \(28\times 28\) & 112 & 3 \\
7 & MBConv6, k5x5 & \(14\times 14\) & 192 & 4 \\
8 & MBConv6, k3x3 & \(7\times 7\) & 320 & 1 \\
9 & Conv1x1 \& Pooling \& FC & \(7\times 7\) & 1280 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: EfficientNet v2 B0 architecture [23]. Figure 1: ResNet50 architecture [22]. Numerical simulations consisted of a model of an elastic object representing an RBC embedded in a flowing fluid and interactions between individual objects (cell-cell and cell-wall/obstacle). The model utilized the lattice-Boltzmann method to represent the fluid, a spring network model to simulate the cell membrane, and a dissipative version of the Immersed Boundary Method (IBM) to connect them. In addition to the fluid force, elastic forces are exerted on the cell mesh points that are evaluated from the deformation of the cell. The resultant force \(\vec{F}_{tot}\) is the driving force according to which the mesh points are propagated in space following [PERSON]'s equation  
\[m\frac{\partial^{2}\vec{x}(t)}{\partial t^{2}}=\vec{F}_{tot},\]  
where \(m\) is the mass of the mesh points. The sources of are the elasto-mechanical properties of the cell membrane, the fluid-cell interaction or possibly other external stimuli. All simulations were performed under consistent channel and fluid flow parameters. The channel had a cuboid shape with dimensions of 104 \(\times\) 60 \(\times\) 40 \(\upmu\)m, and the fluid was discretized into a three-dimensional grid with a spatial step of 1 \(\upmu\)m. The elasticity of RBCs becomes most apparent when they come into contact with other objects. Therefore, we designed a simulated channel topology where RBCs flow through a space with obstacles. The simulated channel featured five cylinders, acting as obstacles that restricted the area of blood flow and induced the manifestation of RBC elasticity (see Figure 2). This channel design aimed to replicate a realistic laboratory environment. The kinematic viscosity of the fluid was \(1.3\times 10^{-6}\,\mathrm{m}^{2}/\mathrm{s}\), and the density was \(1.025\times 10^{3}\,\mathrm{kg}/\mathrm{m}^{3}\). To initiate fluid flow, external forces were applied, with values chosen to achieve a maximum velocity of approximately 0.03 m/s. Cell-cell interactions were simulated using the membrane_collision potential, while interactions between cells and the channel walls were modeled using the soft_sphere potential. RBCs were represented by a surface network consisting of 374 nodes. The elastic properties of the cells were simulated using five types of elastic forces (shown in Table 2, each corresponding to a different elastic modulus. \begin{table}
\begin{tabular}{c c} \hline \hline
**Coefficient** & **Value** \\ \hline stretching coefficient (\(k_{s}\)) & \(5\times 10^{-6}\) N/m \\ bending coefficient (\(k_{b}\)) & \(3\times 10^{-19}\) Nm \\ coefficient of local area conservation (\(k_{al}\)) & \(2\times 10^{-5}\) N/m \\ coefficient of global area conservation (\(k_{ag}\)) & \(7\times 10^{-4}\) N/m \\ coefficient of volume conservation (\(k_{v}\)) & 900 N/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the used simulation parameters. Figure 2: Scheme of the simulation microfluidic channel with cylindrical obstacles. In this study, we focused on four levels of RBC elasticity.