An ensemble approach for multi-stage transfer learning models for COVID-19 detection from chest CT scans.
The novel coronavirus outbreak of 2019 reached pandemic status in March 2020. Since then, many countries have joined efforts to fight the COVID-19 pandemic. A central task for governments is the rapid and effective identification of COVID-19 positive patients. While many molecular tests currently exist, not all hospitals have immediate access to these. However, CT scans, which are readily available at most hospitals, offer an additional method to diagnose COVID-19. As a result, hospitals lacking molecular tests can benefit from it as a way of mitigating said shortage. Furthermore, radiologists have come to achieve accuracy levels over 80% on identifying COVID-19 cases by CT scan image analysis. This paper adds to the existing literature a model based on ensemble methods and 2-stage transfer learning to detect COVID-19 cases based on CT scan images, relying on a simple architecture, yet complex enough model definition, to attain a competitive performance. The proposed model achieved an accuracy of 86.70%, an F1 score of 85.86% and an AUC of 90.82%, proving capable of assisting radiologists with COVID-19 diagnosis. Code developed for this research can be found in the following repository: https://github.com/josehernandezsc/COVID19Net.
['Hernández Santa Cruz|Jose Francisco|JF|']
Ensemble methods Artificial neural networks COVID-19 intelligent system CT scan Visual recognition Coronavirus detection Deep transfer learning