Enhancing Blood Flow Assessment in Diffuse Correlation Spectroscopy: A Transfer Learning Approach with Noise Robustness Analysis

Diffuse correlation spectroscopy (DCS) is an emerging noninvasive technique that measures the tissue blood flow, by using near-infrared coherent point-source illumination to detect spectral changes. While machine learning has demonstrated significant potential for measuring blood flow index (BFi), an open question concerning the success of this approach pertains to its robustness in scenarios involving deviations between datasets with varying Signal-to-Noise Ratios (SNRs) originating from diverse clinical applications and various setups. This study proposes a transfer learning approach, aims to assess …

analysis assessment correlation cs.lg eess.sp flow index machine machine learning measuring near noise noninvasive question robustness spectroscopy success transfer transfer learning