Naslov
Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients

Autori
Štifanić, Daniel ; Musulin, Jelena ; Jurilj, Zdravko ; Baressi Šegota, Sandi ; Lorencin, Ivan ; Anđelić, Nikola ; Vlahinić, Saša ; Šušteršič, Tijana ; Blagojević, Anđela ; Filipović, Nenad ; Car, Zlatan

Izvornik
EAI Endorsed Transactions on Bioengineering and Bioinformatics (2709-4111) 21 (2021), 3; E3, 8

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
artificial intelligence ; COVID-19 ; DeepLabv3+ ; semantic segmentation ; X-ray images

Sažetak
INTRODUCTION: As a result of this global health crisis caused by the COVID-19 pandemic, the medical industry is searching for innovations that have the potential to automate the diagnostic process of COVID-19 and serve as an assistive tool for clinicians. OBJECTIVES: X-ray images have shown to be useful in the diagnosis of COVID-19. The goal of this research is to demonstrate an approach for automatic segmentation of lungs in chest X-ray images. METHODS: In this research DeepLabv3+ with Xception_65, MobileNetV2, and ResNet101 as backbones are used in order to perform lung segmentation. RESULTS: The proposed approach was experimented on X-ray images and has achieved an average mIOU of 0.910, F1 of 0.925, accuracy of 0.968, precision of 0.916, sensitivity of 0.935, and specificity of 0.977. CONCLUSION: Based on the obtained results, the proposed approach proved to be successful in terms of lung segmentation in chest X-ray images and has a great potential for clinical use.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti, Kliničke medicinske znanosti, Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje)

POVEZANOST RADA