Christopher Hui-Kang Nah, Weiling Wu, Samuel Ken-En Gan, Scott Wei-Gen Wong
Published on 28 March 2022
2021 witnessed subsequent waves of COVID-19 sweeping across the world. As the number of daily cases rose in many countries, many adopted the utilization of antigen rapid test (ART) kits for faster detection and isolation of the infected. However, the accuracy of the ART can be impacted by incorrect usage and self-reporting biases. Despite self-administration, image processing of submitted images could be leveraged for validation. Given the ubiquitous use of the smartphone camera, mobile applications that included features such as user uploading of ART kit result images, facilitate verification by backend servers against incorrect self-reported ART results while improving compliance rates. For this purpose, we describe an algorithm that was incorporated into the ‘ART Buddy’ app for the classification of submitted positive and negative ART images. The algorithm was based on machine learning using the Convolutional Neural Network (CNN) to achieve an accuracy of 97.57%, precision of 79.31%, and recall of 88.46%.
2022 Nah CHK et al