ENHANCE CATEGORISATION OF MULTILEVEL HIGH-SENSITIVITY CARDIOVASCULAR BIOMARKERS FROM LATERAL FLOW IMMUNOASSAY IMAGES VIA NEURAL NETWORKS AND DYNAMIC TIME WARPING

Lateral Flow Immunoassays (LFA) are low cost, rapid and highly efficacious Point-of-Care devices. Traditional LFA testing faces challenges to detect high-sensitivity biomarkers due to low sensitivity. Most approaches are based on averaging image intensity from a region-of-interest (ROI). This paper presents a novel system that considers each row of an LFA image as a time series signal and, consequently, does not require the detection of ROI. Long Short-Term Memory (LSTM) networks are used to classify LFA data obtained from multiscale high-sensitivity cardiovascular biomarkers. Dynamic Time Warping (DTW) is incorporated with LSTM to align the LFA data from different concentration levels to a common reference before feeding the distance maps into an LSTM network. The LSTM network outperforms other classifiers with or without DTW. Furthermore, performance of all classifiers is improved after incorporating DTW. The positive outcomes suggest the potential of the proposed system for early risk assessment of cardiovascular diseases.