Systematic literature review: Feasibility analysis for the detection and diagnosis of Covid-19, applying Artificial Intelligence (AI) models

Authors

  • Jonathan Ricardo Tillaguango Jiménez Carrera de Ingeniería en Sistemas/Computación, Universidad Nacional de Loja, Loja, Ecuador

DOI:

https://doi.org/10.54753/cedamaz.v11i2.1183

Keywords:

Diagnostics, Covid-19, CNN, VGG16, Lung radiographs, X-rays.

Abstract

Since the declaration of the health emergency caused by Covid-19 in March 2020, to date, there are approximately 219 million infected people, of which 4.5 million have died. In our country, it is estimated that there are 508 thousand confirmed cases and approximately 32 thousand deaths due to this disease. Despite the availability of verified methods to diagnose Covid-19, Polymerase Chain Reaction (PCR) or Real Time-PCR (RT-PCR) tests tend to generate false positives and negatives between 30\% and 40\%. Therefore, helping traditional methods to make an accurate clinical diagnosis, using lung radiographs as input data, represents a radical change in the detection of Covid-19, since it is a much more comfortable alternative for the patient and, more importantly, increases the level of accuracy while reducing false positive and negative rates. The present Systematic Literature Review (SLR), which is based on Barbara Kitchenham's methodology, seeks to support the creation of a model based on Convolutional Neural Network (CNN) architecture, capable of analyzing pulmonary radiographs for the diagnosis of Covid-19. As a result, it was possible to answer the three research questions posed, which served to delimit the present study, for which 41 related works (TR) were analyzed, which focused on different diagnostic methods based on Artificial Intelligence (AI), however 16 of these TR referred to the use of CNN for the diagnosis of Covid-19 through the analysis of computed tomography (CT) and pulmonary radiographs (X-rays), the latter being the most viable option to apply it in our environment, due to the availability of data. Furthermore, the use of resources by these methods is affordable, either locally using the Nvidia Graphics Processing Unit (GPU) and RAM memory greater than 8GB as a base, or using cloud processing using Google Colab.

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Published

2021-12-24

How to Cite

Tillaguango Jiménez, J. R. . (2021). Systematic literature review: Feasibility analysis for the detection and diagnosis of Covid-19, applying Artificial Intelligence (AI) models. CEDAMAZ, 11(2), 142–151. https://doi.org/10.54753/cedamaz.v11i2.1183

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Vol. 11 No. 2 (2021): CEDAMAZ

Section

Review articles

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