A systematic scoping review of the analysis of COVID-19 disease using chest X-ray images with deep learning models

Abstract

The significance of chest X-ray data in screening patients for COVID-19 has been recognised by medical experts. Deep learning (DL) technologies, particularly artificial intelligence (AI) algorithms, have emerged as efficient classifiers for diagnosing disease through the inspection of chest X-rays. Medical professionals may use deep learning skills to effectively allocate resources and prioritise patients, ensuring that people in critical need of medical attention receive it on time. In reviewed papers, chest X-ray images datasets are used in order to investigate if trained convolutional neural networks (CNNs) can be utilized to accurately classify COVID-19 cases. The study is made more fascinating by the availability of many kinds of new DL models designed specifically for this specific purpose. As the findings illustrate the efficacy of fine-tuned pretrained CNNs for COVID-19 identification using chest X-ray data, the usage of AI-based approaches for COVID-19 identification using chest X-ray data should see substantial growth, giving a more quick and cost-effective approach. The combination of CNN technology and the diagnostic capacity of chest X-ray imaging offers a lot of promise in the fight against COVID-19. Ultimately, the goal is to reduce the strain on healthcare resources and improve patient outcomes by providing medical practitioners with dependable technologies, such as those based on the artificial intelligence (AI), that can aid in real-time monitoring, rapid diagnosis, and patient triage. These advancements enable more effective use of healthcare resources, which benefits patients.