Covid-19 Diagnosis Using Deep Learning From X-Ray and CT Images – Overview

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dc.citation.epage132
dc.citation.issue2
dc.citation.spage126
dc.contributor.affiliationLublin University of Technology
dc.contributor.affiliationDanylo Halytsky Lviv National Medical University
dc.contributor.authorMichalska-Ciekańska, Magdalena
dc.contributor.authorBoyko, Oksana
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2024-02-19T09:44:35Z
dc.date.available2024-02-19T09:44:35Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractSince the outbreak of the pandemic in 2019, Covid-19 has become one of the most important topics in the field of medicine. This disease, caused by the SARS- CoV-2 virus, can lead to serious respiratory diseases and other complications. They can even lead to death. In recent years, the number of Covid-19 cases around the world has increased significantly, resulting in the need for rapid and effective diagnosis of the disease. Currently, the use of deep learning in medical diagnostics is becoming more and more common. It provides the high diagnostic efficacy that scientists, doctors and patients care about. During the Covid-19 diagnostic procedure, most clinicians order images from X- ray and CT to be taken from patients. It is the analysis of these images that gives a full diagnosis. In this article, we will discuss the use of deep neural networks in the diagnosis of Covid-19, especially using chest images taken from X-ray and CT.
dc.format.extent126-132
dc.format.pages7
dc.identifier.citationMichalska-Ciekańska M. Covid-19 Diagnosis Using Deep Learning From X-Ray and CT Images – Overview / Magdalena Michalska-Ciekańska, Oksana Boyko // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 8. — No 2. — P. 126–132.
dc.identifier.citationenMichalska-Ciekańska M. Covid-19 Diagnosis Using Deep Learning From X-Ray and CT Images – Overview / Magdalena Michalska-Ciekańska, Oksana Boyko // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 8. — No 2. — P. 126–132.
dc.identifier.doidoi.org/10.23939/acps2023.02.126
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61340
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofAdvances in Cyber-Physical Systems, 2 (8), 2023
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dc.relation.referencesenN. Chen, M. Zhou, X. Dong et al., "Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study," The Lancet, 395 (10223), 2020, pp. 507–513. DOI: 10.1016/S0140-6736(20)30211-7
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dc.relation.referencesenT. Struyf, J. J. Deeks, J. Dinnes et al., "Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease," Cochrane Database of Systematic Reviews 7 (7), 2020.
dc.relation.referencesenOrganization, W. H., WHO Coronavirus Disease (COVID- 19) Dashboard, World Health Organization, Geneva, Swit- zerland, 2020, http://Https://Covid19.%20Who.%20Int/
dc.relation.referencesenL. Wang, Z. Lin, and A. Wang, "COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images," Scientific Reports 10, Article ID 19549, 2020.
dc.relation.referencesenT. Ai, Z. Yang, H. Hou et al., "Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases," Radiology, 296 (2), Arti- cle ID 200642, 2020. DOI: 10.1148/radiol.2020200642
dc.relation.referencesenR. Tadeusiewicz, M. Szaleniec: "Leksykon sieci neuronowych", Wydawnictwo Fundacji Projekt Nauka, Wrocław, 2015.
dc.relation.referencesenSieć neuronowa. https://pl.wikipedia.org/wiki/Sie%P.4%87_neuronowa [accessed on 30.03.2022]
dc.relation.referencesenW. Zhang, "Shift–invariant pattern recognition neural network and its optical architecture", Proceedings of Annual Conference of the Japan Society of Applied Physics, 1998.
dc.relation.referencesenF. Chollet, "Deep Learning. Praca z językiem Python i biblioteką Keras", Helion, 2019
dc.relation.referencesenM. Mamczur, "Jak działają konwolucyjne sieci neuronowe", https://miroslawmamczur.pl/jak-dzialaja-konwolucyjne-siecineuronowecnn/[ accessed on 17.05.2022]
dc.relation.referencesenR. Dzierżak, "Zastosowanie deep learningu w analizie obrazów medycznych" in Prace doktorantów Wydziału Elektrotechniki i Informatyki Politechniki Lubelskiej, 2018, 59–70
dc.relation.referencesenX. Lu, Y. Firoozeh Abolhasani Zadeh, "Deep learning- based classification for melanoma detection using XceptionNet", Journal of Healthcare Engineering 2022, 2196096. DOI:10.1155/2022/2196096
dc.relation.referencesenX. Lu, Y. Firoozeh Abolhasani Zadeh, "Deep learning- based classification for melanoma detection using XceptionNet", Journal of Healthcare Engineering 2022, 2196096. DOI: 10.1155/2022/2196096
dc.relation.referencesenS. Chaturvedi, J. Tembhurne, T, Diwan, "A multi-class skin cancer classification using deep convolutional neural networks". Multimedia Tools and Applications 2020, 79. DOI:10.1007/s11042-020-09388-2
dc.relation.referencesenS. Dinggang, W. Guorong., S. Heung-Il, "Deep Learning in Medical Image Analysis", The Annual Review of Bio- medical Engineering, 2017, 9, pp. 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
dc.relation.referencesenG. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian., J. van der Laak, B. Ginneken B., C. Sánchez, "A survey on deep learning in medical image analysis", Medical Image Analysis 2017, 42, pp. 60–88. DOI: 10.1016/j.media.2017.07.005
dc.relation.referencesenM. Avendi, A. Kheradvar, H. Jafarkhani, "A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI", Medical Image Analysis 2016, 30, pp.108–119. DOI: 10.1016/j.media.2016.01.005
dc.relation.referencesenA. Krizhevsky, I. Sutskever, G. Hinton, "Imagenet classification with deep convolutional neural networks", Proceedings of the Advances in Neural Information Process- ing Systems, 2012, pp. 1097–1105. DOI:10.1145/3065386
dc.relation.referencesenR. Siegel, D. Naishadham, A. Jemal, "Cancer statistics", CA Cancer J Clin., 2013, 63(1), pp. 11–30. DOI: 10.3322/caac.21166
dc.relation.referencesenT. Alafif, A. Tehame, S. Bajaba, A. Barnawi, S. Zia, "Ma- chine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Direc- tions", Int. J. Environ. Res. Public Health 2021, 18, 1117. DOI: 10.3390/ijerph18031117
dc.relation.referencesenY. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, 521 (7553),2015, pp. 436–444. https://doi.org/10.1038/nature14539.
dc.relation.referencesenM. Scudellari, Hospitals Deploy AI Tools to Detect COVID- 19 on Chest Scans, 2020. Available online: https://spectrum.ieee.org/the-human-os/biomedical/imaging/hospitals-deploy-ai-tools-detect-covid19-chest-scans (accessed on 10 September 2020).
dc.relation.referencesenS. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, M. Cai, J. Yang, Y. Li, X. Meng, et al. "A deep learning algo- rithm using CT images to screen for Corona Virus Disease (COVID-19)", medRxiv 2020. DOI: 10.1007/s00330-021- 07715-1
dc.relation.referencesenJ. Cohen, L. Dao, P. Morrison, K. Roth, Y. Bengio, B. Shen, A. Abbasi, M. Hoshmand-Kochi, M. Ghassemi, H. Li, et al. "Predicting covid-19 pneumonia severity on chest X-ray with deep learning", arXiv 2020, arXiv:2005.11856. https://doi.org/10.48550/arXiv.2005.11856
dc.relation.referencesenBBVA. Artificial Intelligence to detect COVID-19 in Less than a Second Using X-rays. 2019. Available online: https://www.bbva.com/en/artificial-intelligence-to-detect-covid-19-in-less-than-a-second-using-x-rays/ (accessed on 6 September 2020).
dc.relation.referencesenM. Pandit, S. Banday, "SARS n-CoV2-19 detection from chest X-ray images using deep neural networks", Int. J. Pervasive Comput. Commun. 2020, 16, pp.419–427. DOI:10.1108/ijpcc-06-2020-0060
dc.relation.referencesenS, Basu, S. Mitra, "Deep Learning for Screening COVID-19 using Chest X-ray Images", arXiv 2020, arXiv:2004.10507 https://doi.org/10.48550/arXiv.2004.10507
dc.relation.referencesenL. Wang and A. Wong, "COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images," arXiv preprint arXiv:2003.09871, 2020. DOI: 10.1038/s41598- 020-76550-z
dc.relation.referencesenS. Basu and S. Mitra, "Deep Learning for Screening COVID-19 using Chest X-Ray Images," arXiv preprint arXiv:2004.10507, 2020. DOI: 10.1038/s41598-020-76550-z
dc.relation.urihttp://Https://Covid19.%20Who.%20Int/
dc.relation.urihttps://pl.wikipedia.org/wiki/Sie%C4%87_neuronowa
dc.relation.urihttps://miroslawmamczur.pl/jak-dzialaja-konwolucyjne-siecineuronowecnn/
dc.relation.urihttps://doi.org/10.1146/annurev-bioeng-071516-044442
dc.relation.urihttps://doi.org/10.1038/nature14539
dc.relation.urihttps://spectrum.ieee.org/the-human-os/biomedical/imaging/hospitals-deploy-ai-tools-detect-covid19-chest-scans
dc.relation.urihttps://doi.org/10.48550/arXiv.2005.11856
dc.relation.urihttps://www.bbva.com/en/artificial-intelligence-to-detect-covid-19-in-less-than-a-second-using-x-rays/
dc.relation.urihttps://doi.org/10.48550/arXiv.2004.10507
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.rights.holder© Michalska-Ciekańska M., Boyko O., 2023
dc.subjectdeep learning
dc.subjectconvolutional neural networks
dc.subjectCovid-19 diagnostics
dc.subjectCT scans
dc.subjectchest X-ray
dc.titleCovid-19 Diagnosis Using Deep Learning From X-Ray and CT Images – Overview
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2023. – Vol. 8, No. 2