ABSTRACT
Covid-19 global pandemic continues to devastate health care systems across the world. At present, the Covid-19 testing is costly and time-consuming. Chest X-Ray (CXR) testing can be a fast, scalable, and non-invasive method. The existing methods suffer due to the limited CXR samples available from Covid-19. Thus, inspired by the limitations of the open-source work in this field, we propose attention guided contrastive CNN architecture (AC-CovidNet) for Covid-19 detection in CXR images. The proposed method learns the robust and discriminative features with the help of contrastive loss. Moreover, the proposed method gives more importance to the infected regions as guided by the attention mechanism. We compute the sensitivity of the proposed method over the publicly available Covid-19 dataset. It is observed that the proposed AC-CovidNet exhibits very promising performance as compared to the existing methods even with limited training data. It can tackle the bottleneck of CXR Covid-19 datasets being faced by the researchers. The code used in this paper is released publicly at https://github.com/shivram1987/AC-CovidNet/. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.