No abstract

The COVID-19 outbreak has been world-shattering. Since the day it was discovered, it has challenged the world to develop and invent new approaches and methods to fight against it. With this being said, scientists and researchers are relentlessly working to make the situation better and easier for everybody. In this paper, we have utilised transfer learning models to learn and extract important feature vectors from a CT scan image which can prove to be beneficial in the determination of COVID-19. However, due to the limitation in the amount of CT images available publicly, it was problematic to achieve a high performance deep learning model. To overcome this, we have built a balanced dataset consisting of a total of 11,209 CT scan images combined from three different sources which helped in achieving a diverse set of images. Moreover, we have also developed our own convolutional neural network (CNN) which achieved an accuracy of 97.92% in the prediction of Extensive experiments demonstrate the ability and potential of our proposed approaches in achieving high performance models. VGG16 achieved a significant accuracy of 98.7% which is the highest among all the transfer learning models.