Neural Networks for the Diagnosis of Covid-19 in Chest X-ray Images: A Systematic Review and Meta-Analysis

Daniel Cristobal Andrade-Girón; William Joel Marín-Rodriguez; Flor de María Lioo-Jordán; Gladis Jane Villanueva-Cadenas; Flor de María Garivay-Torres de Salinas

doi:10.4108/eetpht.9.4212

Authors

Daniel Cristobal Andrade-Girón Universidad Nacional José Faustino Sánchez Carrión https://orcid.org/0000-0002-9746-3583
William Joel Marín-Rodriguez Universidad Tecnológica del Perú https://orcid.org/0000-0002-0861-9663
Flor de María Lioo-Jordán Universidad Nacional José Faustino Sánchez Carrión https://orcid.org/0000-0001-6659-8621
Gladis Jane Villanueva-Cadenas Universidad Nacional José Faustino Sánchez Carrión https://orcid.org/0000-0001-6659-8621
Flor de María Garivay-Torres de Salinas Universidad Nacional José Faustino Sánchez Carrión https://orcid.org/0000-0001-6659-8621

DOI:

https://doi.org/10.4108/eetpht.9.4212

Keywords:

prediction, COVID-19, model VGG16, deep learning

Abstract

Introduction: The COVID-19 pandemic has triggered a global crisis with significant repercussions in terms of mortality and an ever-increasing demand for urgent medical care, particularly in emergency care settings. This demand arises mainly from the prevailing need to carry out real-time diagnoses and provide immediate care to patients at high risk of serious complications. With the purpose of addressing this problem in a rigorous manner, we have carried out a systematic review focused on evaluating the effectiveness of models based on neural networks for the diagnosis of COVID-19 from chest x-ray images.

Methods: This review has been carried out through an exhaustive search in various renowned electronic bibliographic databases, such as Scopus, IEEE Xplore, PubMed and ScienceDirect. The search period has been extended until September 2023, culminating in the identification of a total of 1,250 relevant articles.

Results: The culminating phase of our review involved the inclusion of 37 studies that met rigorously established selection criteria. These studies have been the subject of a thorough analysis, where various performance metrics such as accuracy/precision, sensitivity/recall, specificity and the F1 value (F1-score) have been evaluated.

Conclusions: Our results reveal that the VGG16 (Visual Geometry Group 16) model, based on neural networks, has emerged as the most widely adopted, manifesting itself in 13.04% of the total models analyzed and in 16.21% of the models supported by the 37 studies. selected. Notably, this algorithm has exhibited an impressive accuracy of over 99% in predicting the diagnosis of patients with suspected COVID-19.

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