A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images.

The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone.

Choquet Integral and Coalition Game-Based Ensemble of Deep Learning Models for COVID-19 Screening From Chest X-Ray Images.

Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis (CAD) is inevitable. Though, some vaccines have now been authorized worldwide, it will take huge time to reach everyone, especially in developing countries. In this work, we have analyzed Chest X-ray (CXR) images for the detection of the coronavirus.