This study aims to evaluate the effectiveness of using neural networks in the detection of COVID-19 through chest X-rays. Based on a literature review, the methodology for building the neural network will be defined, and it will be trained with data collected from reliable sources and analyzed to evaluate the accuracy of detection. The use of neural networks can be a promising and non-invasive alternative for the diagnosis of COVID-19, especially in regions where PCR tests are scarce or time-consuming. Additionally, the use of neural networks may offer advantages over other forms of diagnosis, such as computed tomography (CT), as chest radiographs are more widely available and less costly. However, it is important to consider the limitations and challenges encountered in using neural networks for this purpose, such as the lack of specificity in mild or asymptomatic cases and the need for quality equipment and trained professionals to interpret the images. This study aims to contribute to the advancement of COVID-19 diagnosis through non-invasive and effective methods, as well as to identify possible limitations and challenges in using neural networks for this purpose.
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