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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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, br. 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 i 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, br. 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, i sur.. Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks. Journal of Personalized Medicine [Internet]. 04.01.2021. [pristupljeno 21.12.2024.];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, br. 1, Siječanj 2021. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:271:812063. [Citirano: 21.12.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:271:812063
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Podaci o radu
Naslov (engleski)Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks
AutorIvan Lorencin
AutorSandi Baressi Šegota
AutorNikola Anđelić
AutorAnđela Blagojević
AutorTijana Šušteršić
AutorAlen Protić
AutorMiloš Arsenijević
AutorTomislav Ćabov
AutorNenad Filipović
AutorZlatan Car
Autorova ustanovaSveučilište u Rijeci
(Fakultet dentalne medicine)
Znanstveno / umjetničko
područje, polje i grana		INTERDISCIPLINARNA PODRUČJA ZNANOSTI
Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje)
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaJournal of Personalized Medicine
Brojčani podacivol. 11, br. 1, 28
e-ISSN2075-4426
DOIhttps://doi.org/10.3390/jpm11010028
URN:NBNurn:nbn:hr:271:812063
Datum objave publikacije2021-01-04
URL dokumentahttps://www.mdpi.com/2075-4426/11/1/28
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2023-01-20 10:45:08