Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks.

Autor:	Lorencin I; Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia., Baressi Šegota S; Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia., Anđelić N; Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia., Blagojević A; Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia.; Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia., Šušteršić T; Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia.; Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia., Protić A; Clinical Hospital Centre, Rijeka, Krešimirova ul. 42, 51000 Rijeka, Croatia.; Faculty of Medicine, University of Rijeka, Ul. Braće Branchetta 20/1, 51000 Rijeka, Croatia., Arsenijević M; Clinical Centre Kragujevac, Zmaj Jovina 30, 34000 Kragujevac, Serbia.; Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovića 69, 34000 Kragujevac, Serbia., Ćabov T; Faculty of Dental Medicine, University of Rijeka, Krešimirova ul. 40, 51000 Rijeka, Croatia., Filipović N; Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia.; Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia., Car Z; Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
Jazyk:	angličtina
Zdroj:	Journal of personalized medicine [J Pers Med] 2021 Jan 04; Vol. 11 (1). Date of Electronic Publication: 2021 Jan 04.
DOI:	10.3390/jpm11010028
Abstrakt:	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.
Databáze:	MEDLINE
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