COVID-19 Identification from Chest X-rays

Iosif Mporas, Prasitthichai Naronglerdrit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Artificial Intelligence and Data Science community has contributed to the global response against the new coronavirus, COVID-19. Significant attention has been given to detection and diagnosis tools with rapid diagnostic tools based on X-rays using deep learning being proposed. In this paper we present an evaluation of several well-known pretrained deep CNN models in a transfer learning setup for COVID-19 detection from chest X-ray images. Two different publicly available datasets were employed and different setups were tested using each of them separately of mixing them. The best performing models among the evaluated ones were the DenseNet, ResNet and Xception models, with the results indicating the possibility of identifying COVID-19 positive cases from chest X-ray images.
Original languageEnglish
Title of host publication 2020 International Conference on Biomedical Innovations and Applications (BIA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9781728170732
ISBN (Print)9781728170749
Publication statusPublished - 4 Nov 2020
EventInternational Conference on Biomedical Innovations and Applications (BIA 2020) - Varna, Bulgaria
Duration: 24 Sept 202027 Sept 2020


ConferenceInternational Conference on Biomedical Innovations and Applications (BIA 2020)
Internet address


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