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Covid-19 global pandemic continues to devastate health care systems acrossthe world. At present, the Covid-19 testing is costly and time-consuming. ChestX-Ray (CXR) testing can be a fast, scalable, and non-invasive method. Theexisting 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, wepropose attention guided contrastive CNN architecture (AC-CovidNet) forCovid-19 detection in CXR images. The proposed method learns the robust anddiscriminative features with the help of contrastive loss. Moreover, theproposed method gives more importance to the infected regions as guided by theattention mechanism. We compute the sensitivity of the proposed method over thepublicly available Covid-19 dataset. It is observed that the proposedAC-CovidNet exhibits very promising performance as compared to the existingmethods even with limited training data. It can tackle the bottleneck of CXRCovid-19 datasets being faced by the researchers. The code used in this paperis released publicly at \url{https://github.com/shivram1987/AC-CovidNet/}.

AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images