Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Series Vol. 5 , 25 May 2023
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COVID-19 has been the most serious public health problem of the past decade. To date, the pandemic has taken a huge toll on the globe in terms of human lives lost, economic impact and increased poverty. However, due to its viral characteristics, determining whether a patient carries COVID-19 is not easy. RT-PCR methods are the gold standard for detecting COVID-19, but their time cost, as well as the need for specific equipment and instrumentation, limit their ubiquity in some medically underdeveloped areas. Chest X-rays, a test with high ubiquity and rapid results, require a certain number of professionals to read and determine whether a patient is likely to have COVID-19. Therefore, it is important to have a system to assist in the determination in areas where professionals are lacking. In this experiment, a convolutional neural network-based machine learning technique was used to create a model for recognizing COVID-19. Although there is no clinical evidence to prove its effectiveness, the model can assist professionals in judgment to a certain extent.
Machine Learning, Computer Vision, COVID-19, Convolutional Neural Networks.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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