Autors: Nikolov, M. I., Tsenov, G. T., Mladenov, V. M.
Title: COVID-19 detection with X-Ray input data
Keywords: neural networks, deep learning, Covid-19, virus detection, i

Abstract: This paper presents a system for COVID-19 detection with chest X-Ray scans as input data. The detection engine is implemented with Deep neural networks. The model of the generated Deep Learning Neural Network is trained with the use of chest X-Ray scans dataset as input data. The trained model was tested with new test image datasets and the results show that it provides a high enough recognition rate, providing that this methodology can be applied for quick and nonintrusive COVID-19 detection.



    IEEE International Conference Automatics and Informatics 2021 (ICAI’21), 2021, Bulgaria, IEEE, DOI 978-1-6654-2661-9/21/$31.00 ©2021 IEEE

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    Цитирания (Citation/s):
    1. Portal-Diaz, J.A., Lovelle-Enríquez, O., Perez-Diaz, M., Lopez-Cabrera, J.D., Reyes-Cardoso, O. and Orozco-Morales, R., 2022. New patch-based strategy for COVID-19 automatic identification using chest x-ray images. Health and Technology, 12(6), pp.1117-1132. (Web of Science, Scopus, Google Scholar) JCI 0.4, SJR 0.36. - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus