Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

作者：

Highlights：

• Proposed automatic COVID screening (ACoS) system for detection of infected patients.

• Random image augmentation is applied to incorporate the variability in the images.

• Applied hierarchical (two phase) classification to segregate three classes.

• Majority vote based classifier ensemble is used to combine model’s prediction.

• Proposed method show promising potential to detect nCOVID-19 infected patients.

摘要

•Proposed automatic COVID screening (ACoS) system for detection of infected patients.•Random image augmentation is applied to incorporate the variability in the images.•Applied hierarchical (two phase) classification to segregate three classes.•Majority vote based classifier ensemble is used to combine model’s prediction.•Proposed method show promising potential to detect nCOVID-19 infected patients.

论文关键词：Coronavirus,Chest X-Ray,nCOVID-19,Pneumonia,SARS-COV-2,Contagious,Pandemic

论文评审过程：Received 6 May 2020, Revised 9 August 2020, Accepted 19 August 2020, Available online 26 August 2020, Version of Record 3 September 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113909