COVID-19 lateral flow test image classification using deep CNN and StyleGAN2 - Pannipulath Venugopal, Vishnu; Babu Saheer, Lakshmi; Maktabdar Oghaz, Mahdi (Frontiers in Artificial Intelligence, 2024)
The adoption of artificial intelligence in the healthcare industry has the potential to significantly improve clinical workflows and diagnoses. This can be particularly beneficial for time-critical large-scale operations, as exemplified by recent mass testing efforts for COVID-19 and the accurate identification and tracking of positive patients. This study presents an automated COVID-19 Rapid Antigen Test Device (RATD) image classification model that relies on deep Convolutional Neural Network (CNN) to differentiate between positive and negative real-world RATD test images. Such a system can greatly expedite the testing process and facilitate a large-scale testing and tracking system. One major challenge in the design and development of such a model is the absence of RATD image dataset for training deep CNN classification models. To address this issue, this study compiled a dataset of RATD images through crowdsourcing using mobile devices. A total of 900 real-world images were captured, with a focus on two specific classes of images: Positive (COVID-19 detected) and Negative (COVID-19 not detected). These images were used to develop a binary CNN classifier. Rigorous data augmentation was performed to further increase the dataset's size. Moreover, StyleGAN2-ADA was used to generate simulated training sample images, primarily to address issues related to dataset class imbalance. The performance of several classification CNN models was investigated, with 1 Venugopal et al.the best model achieving a validation accuracy of 93% and test accuracies of 88% and 82% for simulated and real datasets, respectively. It was observed that further augmenting simulated images during training did not significantly improve the performance of realworld test images; instead, it only improved the performance of simulated test images.The findings of this study can be applied in the deployment of large-scale test and tracking systems to mitigate outbreaks similar to COVID-19.