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projects:year10:10a.002.tau_wp1

10a.002.TAU_WP1 - COVID-19 Severity Grading Using Chest X-Ray Images

Project - Summary

  • Accurate and fast detection of coronavirus disease 2019 (COVID-19) has the utmost importance to prevent the spread of the disease.
  • The proposed Operational Segmentation Network with generative neurons aims to segment COVID-19 pneumonia and discriminate it from other thoracic diseases using chest E-ray (CXR) images.
  • The largest CXR database with around 10,000 COVID-19 positive CXRs for the purpose of COVID-19 pneumonia segmentation and detection is publicly available to the research community.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Moncef Gabbouj PI moncef.gabbouj@tuni.fi 358 40 073 6613 Tampere University
Serkan Kiranyaz Co-PI serkan.kiranyaz@tuni.fi 97 43 063 5600 Tampere University
Aysen Degerli Researcher aysen.degerli@tuni.fi 358 46 521 9737 Tampere University
Ozer Devecioglu Researcher ozer.devecioglu@tuni.fi N/A Tampere University
Christian Sundell Project Mentor Christian.Sundell@tietoevry.com NA TietoEVRY
Iftikhar Ahmad Project Mentor iftikhar.ahmad@tietoevry.com N/A TietoEVRY

Project - Novelty of Approach

See “Project Summary” section above.

Project - Deliverables

Deliverables
1 Integrating OSegNet model into the http://qatacov.live/ website

Project - Benefits to IAB

  • Time-efficient and robust COVID-19 diagnosis that will overcome the spread of the disease.
  • Easy-to-use tool in health care centers, hospitals, and airports for COVID-19 detection.
  • Patient monitoring during treatment via severity grading feature.

Project - Documents

projects/year10/10a.002.tau_wp1.txt · Last modified: 2022/05/13 08:24 by sally.johnson