Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients (CROSBI ID 296495)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Štifanić, Daniel ; Musulin, Jelena ; Jurilj, Zdravko ; Baressi Šegota, Sandi ; Lorencin, Ivan ; Anđelić, Nikola ; Vlahinić, Saša ; Šušteršič, Tijana ; Blagojević, Anđela ; Filipović, Nenad ; Car, Zlatan
engleski
Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients
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.
artificial intelligence ; COVID-19 ; DeepLabv3+ ; semantic segmentation ; X-ray images
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
21 (3)
2021.
e3
8
objavljeno
2709-4111
10.4108/eai.7-7-2021.170287