A Hierarchical Classification System for the Detection of COVID-19 From Chest X-Ray Images

Meghna P Ayyar, Jenny Benois-Pineau, Akka Zemmari; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 519-528

With the ever-increasing cases of the Covid-19 pandemic, it is important to leverage deep learning methods to create tools that can aid in relieving the pressure that is put on the limited resources in most developing countries. In this work, we propose a hierarchical classification system for the classification of Covid-19 from Chest X-Ray (CXR) images following a recent proposal of massive use of this modality instead of CT. The system composed of multiple binary classifiers outperforms a tailor-made multi-class classifier COVID-Net. We also show that using well-known established deep learning frameworks combined with a global attention mechanism outperforms the baseline COVID-Net specifically designed for the classification of Covid-19 from CXR images. Our method shows approximately a 4% improvement in the sensitivity to Covid-19 detection from 91% of COVID-Net to 96%. Using popular networks with the possibility of cross-domain transfer learning ensures that the designing and training times are reduced. Furthermore, well-established frameworks can be faster adapted into an application in clinical practice.