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

Štifanić, D. and Musulin, J. and Jurilj, Z. and Šegota, S. and Lorencin, I. and Anđelić, N. and Vlahinić, S. and Šušteršič, T. and Blagojević, A. and Filipović, N. and Car, Z. (2021) Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients. EAI Endorsed Transactions on Bioengineering and Bioinformatics, 1. e3. ISSN 2709-4111

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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.

Item Type: Article
Uncontrolled Keywords: Artificial Intelligence, COVID-19, DeepLabv3+, Semantic segmentation, X-ray images
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 12 Jul 2021 12:54
Last Modified: 12 Jul 2021 12:54

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