prikaz prve stranice dokumenta Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks
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Scientific paper - Original scientific paper
Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks
Journal of Personalized Medicine, 11 (2021), 1; 28. https://doi.org/10.3390/jpm11010028
Lorencin, Ivan; Baressi Šegota, Sandi; Anđelić, Nikola; Blagojević, Anđela; Šušteršić, Tijana; Protić, Alen; Arsenijević, Miloš; Ćabov, Tomislav; Filipović, Nenad; Car, Zlatan

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Lorencin, I., Baressi Šegota, S., Anđelić, N., Blagojević, A., Šušteršić, T., Protić, A. ... Car, Z. (2021). Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks. Journal of Personalized Medicine, 11. (1). doi: 10.3390/jpm11010028

Lorencin, Ivan, et al. "Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks." Journal of Personalized Medicine, vol. 11, no. 1, 2021. https://doi.org/10.3390/jpm11010028

Lorencin, Ivan, Sandi Baressi Šegota, Nikola Anđelić, Anđela Blagojević, Tijana Šušteršić, Alen Protić, Miloš Arsenijević, Tomislav Ćabov, Nenad Filipović and Zlatan Car. "Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks." Journal of Personalized Medicine 11, no. 1 (2021). https://doi.org/10.3390/jpm11010028

Lorencin, I., et al. (2021) 'Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks', Journal of Personalized Medicine, 11(1). doi: 10.3390/jpm11010028

Lorencin I, Baressi Šegota S, Anđelić N, Blagojević A, Šušteršić T, Protić A, and sur.. Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks. Journal of Personalized Medicine [Internet]. 2021 January 04 [cited 2024 April 25];11(1). doi: 10.3390/jpm11010028

I. Lorencin, et al., "Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks", Journal of Personalized Medicine, vol. 11, no. 1, January 2021. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:271:812063. [Accessed: 25 April 2024]

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Metadata
Title (english)Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks
AuthorIvan Lorencin
AuthorSandi Baressi Šegota
AuthorNikola Anđelić
AuthorAnđela Blagojević
AuthorTijana Šušteršić
AuthorAlen Protić
AuthorMiloš Arsenijević
AuthorTomislav Ćabov
AuthorNenad Filipović
AuthorZlatan Car
Author's institutionUniversity of Rijeka
(Faculty of Dental Medicine)
Scientific / art field,
discipline and subdiscipline		INTERDISCIPLINARY AREAS OF KNOWLEDGE
Biotechnology in Biomedicine (natural science, biomedicine and healthcare, bioethics area
Abstract (english)
COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleJournal of Personalized Medicine
Numberingvol. 11, no. 1, 28
e-ISSN2075-4426
DOIhttps://doi.org/10.3390/jpm11010028
URN:NBNurn:nbn:hr:271:812063
Publication2021-01-04
Document URLhttps://www.mdpi.com/2075-4426/11/1/28
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-01-20 10:45:08