COVID-19 Classification from Chest X-rays Based on Attention and Knowledge Distillation

ABSTRACT

The Coronavirus Disease 2019 (COVID-19) is the pandemic that has had the greatest impact on world economic development in recent years. Early detection is critical to identify patients with COVID-19, chest x-ray is used for early detection is a rapid, extensive and cost-effective method. The existing technology use deep learning methods, and have achieved very good results. However, the training time of deep learning method is long, and the model size makes it difficult to deploy on hardware system. In this work, we have proposed an attention-based ResNet50v2 network, and taken the network as the teacher network to transfer the knowledge to the student network by knowledge distillation. Thus, the student network has higher accuracy and sensitivity to the positive samples of COVID-19 under the condition of low model parameters, high training speed. The experimental results show that our network of teacher and student have achieved 100% accuracy and sensitivity in both COVID-19 and Normal binary classification. In addition, the accuracy rate of teacher network is 98.20%, the sensitivity is 99.58%, the accuracy rate of student network is 97.68%, the sensitivity is 99.17% in the COVID-19, Viral pneumonia and Normal multiple classification, and the parameters of the student network are only 0.269M. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.