Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images

Saúl Calderón Ramírez, Shengxiang Yang, Armaghan Moemeni, David A. Elizondo, Simon Colreavy-Donnelly, Luis Fernando Chavarria-Estrada, Miguel A. Molina-Cabello

Abstract: Highlights•COVID-19 detection deep learning architectures typically need many labels.•Also the datasets at the beginning of a virus outbreak are highly imbalanced.•Semi supervised data can be used to increase model’s accuracy with few labels.•The effect of data imbalance on semi-supervised learning is under-explored.•A method to correct data imbalance for semi supervised learning is proposed.