  The widely used gene quantisation technique, Lateral Flow Device (LFD), is
now commonly used to detect the presence of SARS-CoV-2. It is enabling the
control and prevention of the spread of the virus. Depending on the viral load,
LFD have different sensitivity and self-test for normal user present additional
challenge to interpret the result. With the evolution of machine learning
algorithms, image processing and analysis has seen unprecedented growth. In
this interdisciplinary study, we employ novel image analysis methods of
computer vision and machine learning field to study visual features of the
control region of LFD. Here, we automatically derive results for any image
containing LFD into positive, negative or inconclusive. This will reduce the
burden of human involvement of health workers and perception bias.
