The Novel Coronavirus 2019 (COVID-2019) spread quickly around the planet and turned into an undermining pandemic. The early detection of Covid disease is one of the principal challenges and needs on the planet. Early detection helps in controlling the spread of infection. Deep learning has acquired great significance in clinical picture investigation, illustrating improved performance compared to conventional machine learning framework. In this work, a DL based model is proposed for recognizing and characterizing the inconsistencies in chest X-Ray pictures and arranging as unaffected, Covid affected, or Pneumonia. Considering the information inadequacy in clinical space, VGG architecture is utilized as the pre-trained models for building the model for recognition. The quality of the X-Ray pictures and noise present in the pictures influence the decision making leading to highfalse positives and false negatives. In the current model, pre-processed pictures are fed as input to the DL model accomplishing a maximum accuracy of 96.56%. The proposed model outperforms the DL model without pre-processing with a false positive rate of 0.024 and a false negative rate of 0.026.