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

arXiv: https://arxiv.org/abs/2205.13311 · HTML: https://mlsanity.com/paper/2205.13311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [modAL: A modular active learning framework for Python](https://mlsanity.com/paper/1805.00979/) (2018)

## Abstract (arXiv)

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.