Estimating the deposition efficiency of micro-particles in human upper airway using computerized tomography imaging and bio-inspired evolvable extreme learning machine interpolator

Abstract

In this investigation, an intelligent technique is used to analyze the deposition efficiency (DE) of micro-particles in a realistic human upper airway model. To do so, firstly a numerical framework including computerized tomography (CT) imaging, geometry production software and mesh generation tool is utilized to provide a bed for simulating the human airway. Thereafter, the simulated airway is exposed to computational fluid-particle dynamics (CFPD) system to study the complete upper trachea-bronchial airway from trachea (G0) to second generation of bifurcations (G2). At the numerical phase, low Reynolds number (LRN) k-ω turbulence model is considered to simulate the laminar to turbulent occurred airflow. At the final stage, the obtained knowledge is used to provide a continuous model suited for analyzing the DE value in different sections of human upper airway. Here, the authors utilize a fast yet accurate intelligent interpolator called extreme learning machine (ELM) neural network to capture the knowledge of the obtained database. Bio-inspired metaheuristics are also used to evolve the architecture of ELM neural network such that it can predict the DE values with a high degree of accuracy and robustness. The results of the conducted experiments indicate that the proposed bio-inspired ELM interpolator is capable of providing a fast and accurate model which can yield proper information in a very short period of time as compared to the existing numerical techniques such as CFD. On the other hand, by using different types of bio-inspired metaheuristics, i.e. mutable smart bee algorithm (MSBA), particle swarm optimization (PSO), firefly algorithm (FA) and scale-factor local search differential evolution (SFLSDE), it is observed that the structure of ELM neural network can be easily trained using metaheuristics. In general, the experimental results demonstrate the applicability and efficacy of soft computing techniques for predicting the DE values in human upper airway.