COVID-19/Pneumonia Classification Based on Guided Attention

Abstract With the novel coronavirus 19 (COVID-19) continually having a devastating effect around the globe, many scientists and clinicians are actively seeking to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the health care industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a robust method for the detection of COVID-19 by utilizing patients' chest X-ray images. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combing publicly available data across 5 different sources and carefully annotating the comprising images into three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on the directed guidance of traditional classification networks, where the guidance is directed by an external segmentation network. Through this network, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models specifically for COVID-19, furthermore one network, VGG-16, outperformed the best of the tailor-made models.