Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)
Series Vol. 5 , 25 May 2023
Open
Access | Article
Detection for COVID-19 Chest X-ray Based on Convolutional Neural Network
* Author to whom correspondence should be addressed.
Abstract
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.
Keywords
Machine Learning, Computer Vision, COVID-19, Convolutional Neural Networks.
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)
- ISBN (Print)
- 978-1-915371-53-9
- ISBN (Online)
- 978-1-915371-54-6
- Published Date
- 25 May 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/5/20230388
- Copyright
- © 2023 The Author(s)
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited