Comparison of Transfer Learning Models for COVID-19 Detection using Chest X-Ray Images

The transmission of cases of the COVID-19 virus (Coronavirus disease) is currently still racing. Even though the COVID-19 recovery rate has increased, new variants such as Omicron or Centaurus are still spreading in various countries. Detection COVID-19 based on Chest X-Ray is needed to avoid the wider transmission of the virus. This study uses the CNN (Convolutional Neural Network) transfer learning models like AlexNet, VGG19, Resnet50, InceptionV3 and the BasicCNN model. This study uses a dataset named the COVID-19 Radiography Database which contains three classes, namely COVID-19, Normal, and Viral Pneumonia. The results of this study indicate that the Resnet50 model is the best model that produces the highest accuracy compared to other models. The Resnet50 model obtained an accuracy of 98.68%. Then followed by other models in sequence, namely InceptionV3, VGG19, AlexNet, and BasicCNN. Evaluation in this study also uses Precision, Recall, and F1-Measure which show that Resnet50 obtains the highest value compared with other methods. This shows that the transfer learning model has a good performance for detection of COVID-19 based on Chest X-Ray.