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

SARS-CoV-2 Result Interpretation based on Image Analysis of Lateral Flow Devices