A customizable automated container-free multi-strip detection and line recognition system for colorimetric analysis with lateral flow immunoassay for lean meat powder based on machine vision and smartphone

A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones

“…Qi Qin et al adopted the U-net algorithm in deep learning and achieved the highest accuracy of 97.46% with 942 samples [ 21 ]. Guanao Zhao et al proposed a multi-strip detection algorithm using image processing, which achieved an accuracy of 94.5% [ 22 ]. Compared to traditional image processing algorithms, the accuracy is slightly lower than KNN and U-net but slightly higher than CNN with insufficient sample size.…”

An intelligent readable and capture-antibody-independent lateral flow immunoassay based on Cu2−xSe nanocrystals for point-of-care detection of Escherichia coli O157:H7

“…11 Lateral flow immunoassay (LFIA) has the advantages of portability, affordability, and user-friendliness, making it one of the most widely used POCT devices. 12 At present, the analysis of LFIA measurement results is mainly achieved by rough reading with the naked eye or grayscale values, which affects the accuracy of the result and cannot be quantitatively detected in real-time. 13 Fortunately, smartphones have played an important role in the application of POCT, which is widely used for data recording and signal processing with high accuracy.…”