ABSTRACT
Since the discovery of COVID-19, new variants have been emerging. The latest in this series is BA.2, which is the subvariant of omicron and is more transmissible than the previous ones. People infected with this virus must be diagnosed at the earliest to provide the needed clinical attention. Radiological images of the chest are crucial in diagnosing the severity of BA.2 infection RT-PCR (Reverse Transcription-Polymerase Chain Reaction) is one of the approved diagnostics for COVID-19. Moreover, it takes time to give the result as compared to imaging techniques like X-ray and CT scans. Deep learning methods offer a clearer understanding and assist in extracting important data from X-ray images. In the absence of clinical assistance, this research emphasises the benefits of employing deep learning to ascertain the infection's existence. We present a light-weighted convolutional neural network-based deep learning binary classification model in this paper. Dataset consists of 16808 publically available images. The accuracy of our model is 98.76% which is effective to diagnose such patients. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.