  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 \url{https://github.com/shivram1987/AC-CovidNet/}.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning