Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images

The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients' confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers.

Automated summary

The medical and scientific communities are working on treating infected patients and developing vaccines, with machine learning proving to be efficient in healthcare for combatting COVID-19. Hospitals are struggling with increased cases and challenges in assembling quality medical image datasets. This study developed an ensemble-based BoF classification system for COVID-19 detection, showing its superiority in classifying normal and COVID-19 chest X-ray images compared to other classifiers.