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Pregled bibliografske jedinice broj: 1135645

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


Štifanić, Daniel; Musulin, Jelena; Jurilj, Zdravko; Baressi Šegota, Sandi; Lorencin, Ivan; Anđelić, Nikola; Vlahinić, Saša; Šušteršič, Tijana; Blagojević, Anđela; Filipović, Nenad; Car, Zlatan
Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients // EAI Endorsed Transactions on Bioengineering and Bioinformatics (2021) doi:10.4108/eai.7-7-2021.170287 (znanstveni, online first)


CROSBI ID: 1135645 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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

Vrsta, podvrsta
Radovi u časopisima, znanstveni

Izvornik
EAI Endorsed Transactions on Bioengineering and Bioinformatics (2021)

Status rada
Online first

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


Projekti:
EK-KF-KK.01.1.1.01.0009-2 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima - IJ za napredne kooperativne sustave (DATACROSS) (Petrović, Ivan; Šmuc, Tomislav, EK - KK.01.1.1.01) ( POIROT)
EK-EFRR-KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša, EK - KK.01.2.2.03) ( POIROT)
Ostalo-CEI - 305.6019-20 - Use of regressive artificial intelligence (AI) and machine learning (ML) methods in modelling of COVID-19 spread (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( POIROT)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( POIROT)

Ustanove:
Tehnički fakultet, Rijeka,
Klinički bolnički centar Rijeka

Profili:

Avatar Url Nenad Filipović (autor)

Avatar Url Zlatan Car (autor)

Avatar Url Nikola Anđelić (autor)

Avatar Url Saša Vlahinić (autor)

Avatar Url Ivan Lorencin (autor)

Citiraj ovu publikaciju

Štifanić, Daniel; Musulin, Jelena; Jurilj, Zdravko; Baressi Šegota, Sandi; Lorencin, Ivan; Anđelić, Nikola; Vlahinić, Saša; Šušteršič, Tijana; Blagojević, Anđela; Filipović, Nenad; Car, Zlatan
Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients // EAI Endorsed Transactions on Bioengineering and Bioinformatics (2021) doi:10.4108/eai.7-7-2021.170287 (znanstveni, online first)
Štifanić, D., Musulin, J., Jurilj, Z., Baressi Šegota, S., Lorencin, I., Anđelić, N., Vlahinić, S., Šušteršič, T., Blagojević, A., Filipović, N. & Car, Z. (2021) Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients. Prihvaćen za objavljivanje u EAI Endorsed Transactions on Bioengineering and Bioinformatics. [Preprint] doi:10.4108/eai.7-7-2021.170287.
@unknown{unknown, year = {2021}, DOI = {10.4108/eai.7-7-2021.170287}, keywords = {artificial intelligence, COVID-19, DeepLabv3+, semantic segmentation, X-ray images}, journal = {EAI Endorsed Transactions on Bioengineering and Bioinformatics}, doi = {10.4108/eai.7-7-2021.170287}, title = {Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients}, keyword = {artificial intelligence, COVID-19, DeepLabv3+, semantic segmentation, X-ray images} }
@unknown{unknown, year = {2021}, DOI = {10.4108/eai.7-7-2021.170287}, keywords = {artificial intelligence, COVID-19, DeepLabv3+, semantic segmentation, X-ray images}, journal = {EAI Endorsed Transactions on Bioengineering and Bioinformatics}, doi = {10.4108/eai.7-7-2021.170287}, title = {Semantic segmentation of chest X-ray images based on the severity of COVID-19 infected patients}, keyword = {artificial intelligence, COVID-19, DeepLabv3+, semantic segmentation, X-ray images} }

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