Fast Cerebral Blood Flow Analysis via Extreme Learning Machine
This provides a rapid and precise method for medical researchers analyzing cerebral blood flow, though it appears incremental as it applies an existing machine learning technique to a specific domain.
The paper tackles the problem of analyzing cerebral blood flow using Diffuse Correlation Spectroscopy by applying Extreme Learning Machine, achieving higher fidelity across noise levels and optical parameters with significantly faster training speeds compared to neural networks.
We introduce a rapid and precise analytical approach for analyzing cerebral blood flow (CBF) using Diffuse Correlation Spectroscopy (DCS) with the application of the Extreme Learning Machine (ELM). Our evaluation of ELM and existing algorithms involves a comprehensive set of metrics. We assess these algorithms using synthetic datasets for both semi-infinite and multi-layer models. The results demonstrate that ELM consistently achieves higher fidelity across various noise levels and optical parameters, showcasing robust generalization ability and outperforming iterative fitting algorithms. Through a comparison with a computationally efficient neural network, ELM attains comparable accuracy with reduced training and inference times. Notably, the absence of a back-propagation process in ELM during training results in significantly faster training speeds compared to existing neural network approaches. This proposed strategy holds promise for edge computing applications with online training capabilities.