ABSTRACT
Covid 19, caused from coronavirus SAR-CoV-2, is currently a dangerous threat to human beings. The rapid development of the Covid 19 pandemic forced all countries to develop fast and reliable methods to detect the coronavirus SAR-CoV-2. Transfer learning with medical images is a suitable such detecting method. Transfer learning, a deep learning technique, has special abilities such as speed of training, fewer requirements of training data set size and reduced demand of expert domain knowledge. Diagnosing Covid 19 using medical images is also considered by some to be more reliable than using traditional laboratory methods. This paper proposes transfer learning methods combined with medical images to detect Covid 19. Using a Covid 19 X-ray data set from Kaggle, this research considers viral pneumonia as a separate class, increasing the performance since viral pneumonia is often wrongly classified as Covid 19, even by radiologists. This paper uses specialized metrics to deal with the imbalanced nature of the data and visualises results using Local Interpretable Model-agnostic Explanations to indicate areas of images associated with Covid 19. The ResNet family of CNNs performed well, with ResNet 34 performing better than the 18 and 50 layer versions. Inception and DenseNet also have good classification performance. © 2021 IEEE.