Osegnet: Operational Segmentation Network for Covid-19 Detection Using Chest X-Ray Images

Aysen Degerli, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Moncef Gabbouj

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)


Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce datasets bearing the risk of overfitting. Additionally, previous studies have revealed the fact that deep networks are not reliable for classification since their decisions may originate from irrelevant areas on the CXRs. Therefore, in this study, we propose Operational Segmentation Network (OSegNet) that performs detection by segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121, 378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community. Consequently, OSegNet has achieved a detection performance with the highest accuracy of 99.65% among the state-of-the-art deep models with 98.09% precision.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages5
ISBN (Electronic)978-1-66549-620-9
Publication statusPublished - 2022
MoE publication typeA4 Article in a conference publication
Event2022 IEEE International Conference on Image Processing (ICIP) - Bordeaux, France
Duration: 16 Oct 202219 Oct 2022


Conference2022 IEEE International Conference on Image Processing (ICIP)
Abbreviated titleICIP


  • COVID-19
  • Deep Learning
  • Machine Learning
  • SARS-CoV-2


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