Artificial Special Visual Geometry Group-16(VGG) Learning Model for Analysing Accuracy and Precision of SARS-COV-2 Forecasting

Current research attempts to predict SARS-COV-2 efficiently applying recent artificial intelligence algorithms and lung radiography. In this investigation, we applied the promising Visual Geometry Group16 transfer learning technique to identify SARS-COV-2 more reliably and fast. The technique splits the lung radiograph image into two groups: SARS-COV-2 and normal. The Accuracy, Exactness, Recognize, Support, and Harmonic Mean metrics are used to evaluate the model’s effectiveness. A total of 2000 radiograph samples were used in the study. The recommended VGG16 model provides an outstanding calibration for identification of 0.93% for the knowledge involved in the given random sample, which is greater than any current technique reported even in prior studies. Because the proposed method is highly successful and accurate, it might be used to help and educate radiologists and other medical practitioners in diagnosing SARS-COV-2 using lung radiographs.