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 us...

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Bibliographic Details
Published in:EAI endorsed transactions on bioengineering and bioinformatics 2021-08, Vol.1 (3), p.170287
Main Authors: Štifanić, D., Musulin, J., Jurilj, Z., Šegota, S., Lorencin, I., Anđelić, N., Vlahinić, S., Šušteršič, T., Blagojević, A., Filipović, N., Car, Z.
Format: Article
Language:English
Subjects:
Chest
Coronaviruses
COVID-19
Image processing
Image segmentation
Lungs
Medical imaging
Nondestructive testing
Public health
Semantic segmentation
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Summary: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.
ISSN:2709-4111
2709-4111
DOI:10.4108/eai.7-7-2021.170287