Influence of CNN Input Modification for Red Blood Cells Trajectory Prediction in Blood Flow

Autor:	Katarína Bachratá, Hynek Bachraty, Michal Chovanec, Katarina Jasencakova
Rok vydání:	2019
Předmět:	Computational complexity theory Artificial neural network Computer science business.industry Computer Science::Neural and Evolutionary Computation Trajectory Pattern recognition Blood flow Artificial intelligence business Convolutional neural network
Zdroj:	2019 IEEE 15th International Scientific Conference on Informatics.
DOI:	10.1109/informatics47936.2019.9119275
Popis:	Computer simulations of blood flow in microfluidic devices help for their development. These simulations are limited by their computational complexity. A possible solution to this problem is to analyze the simulation output data using machine learning methods. We use convolutional neural networks (CNN) for red blood cells (RBCs) trajectory prediction, which is very important for blood flow modeling. In this paper, we study how miscellaneous modifications of a CNN input affect the results from the learning experiments. All performed neural network experiments have sufficient accuracy. We evaluated, which CNN input parameters and their values have a significant impact on experiments accuracy.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9e87a9bf1a3b27a40e33e1e4a501ae59 https://doi.org/10.1109/informatics47936.2019.9119275 Zobrazit plný text záznamu