## 5 Conclusions  
In this study, we investigated the classification of RBC elasticity using video recordings and CNNs. We addressed the scarcity of available blood flow recordings by employing computer simulations based on a numerical model. Our simulation model successfully captured the behavior of RBCs as elastic objects within a fluid flow, generating a large amount of training data for CNN-based classification. By analyzing the geometric characteristics captured in video recordings, we classified RBC elasticity using different CNN architectures, including ResNet and EfficientNet. Our results demonstrate that CNNs can effectively classify RBC elasticity, providing a potential diagnostic tool for various diseases impacting RBC health. The integration of computer simulations and CNN-based video analysis offers a valuable approach to understanding the behavior of RBCs and developing new diagnostic techniques. The use of simulation-generated data provides a rich source of training examples, enabling the training of CNNs even with limited video recordings. Furthermore, our study compared the performance of different CNN architectures, and the results showed that the EfficientNet model achieved the best accuracy for RBC elasticity classification. However, we observed that overfitting was a challenge for both ResNetand EfficientNet architectures, indicating the need for further regularization techniques to improve the model's generalization performance. We have observed that training the network in a higher target space (four classes) and subsequently mapping the results to a lower target space (two classes) yields the best classification performance for our problem. Another important finding is that cross-validation, where a model trained on a dataset with a different ratio of class examples is validated on another dataset with a different ratio of class examples, results in a significantly lower performance compared to validation on the same type of dataset. This observation highlights the importance of training the model on a dataset with a class ratio that matches the ratio observed in real-world scenarios. In our experiments, the RBC health is determined only by elasticity, but in reality, other RBC properties can also change, such as volume, the shape of the blood cell, etc. For this reason, further research is necessary. In conclusion, our study highlights the potential of combining computer simulations, video analysis, and CNNs for the classification of RBC elasticity. Further research and optimization of CNN architectures and regularization techniques are warranted to enhance the accuracy and robustness of the classification model. The development of accurate and reliable diagnostic tools for assessing RBC health can have significant implications for disease diagnosis and monitoring, ultimately contributing to improved healthcare outcomes. Conceptualization, K.B., H.B. and M.S. ; methodology, S.M. and M.S. ; software, S.M. and M.S. ; validation, S.M. and M.S. ; formal analysis, S.M. ; investigation, S.M., M.S. and P.N. ; resources, M.S. and H.B. ; data curation, M.S. ; writing--original draft preparation, S.M., H.B. and M.S. ; writing--review and editing, H.B., K.B., S.M., M.S. and P.N. ; visualization, S.M. and M.S. ; supervision, K.B. ; project administration, K.B. ; funding acquisition, K.B. All authors have read and agreed to the published version of the manuscript. This research was supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic under the contract No. VEGA 1/0369/22. Not applicable. The data used in this study are freely available in the GIT repository: [[https://github.com/molcan23/RBC_NN](https://github.com/molcan23/RBC_NN)]([https://github.com/molcan23/RBC_NN](https://github.com/molcan23/RBC_NN)) (accessed on 13 July 2022). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: