Edge impulse-based pretrained neural network for diagnosing COVID-19

Abstract

COVID-19 has wreaked havoc on a global scale, primarily owing to its extraordinary contagiousness, thereby straining local healthcare systems to their limits. While the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is known for its specificity, it suffers from time-consuming procedures and a notable false negative rate. Consequently, there is an immediate imperative for a swift and precise diagnostic approach. This paper introduces a novel concept, employing artificial intelligence, to address these challenges effectively. Specifically, this study employed a transfer learning model provided by the Edge Impulse platform and a dataset containing chest X-ray images of children. This study pre-processed these images and trained and tested them several times using different image sizes and network architectures. The experimental results show that the model achieves very high accuracy (>99%) with 160*160 image size and version 1.0 or 0.35 of the network architecture. These results clearly support the hypothesis that migration learning can play an important role in the fast and accurate diagnosis of COVID-19 with appropriate image size and network architecture. This research can be used as a way to rapidly train locally adapted AI models to achieve rapid assisted diagnosis of this type of acute infectious disease.