  In the last few months, the novel COVID19 pandemic has spread all over the
world. Due to its easy transmission, developing techniques to accurately and
easily identify the presence of COVID19 and distinguish it from other forms of
flu and pneumonia is crucial. Recent research has shown that the chest Xrays of
patients suffering from COVID19 depicts certain abnormalities in the
radiography. However, those approaches are closed source and not made available
to the research community for re-producibility and gaining deeper insight. The
goal of this work is to build open source and open access datasets and present
an accurate Convolutional Neural Network framework for differentiating COVID19
cases from other pneumonia cases. Our work utilizes state of the art training
techniques including progressive resizing, cyclical learning rate finding and
discriminative learning rates to training fast and accurate residual neural
networks. Using these techniques, we showed the state of the art results on the
open-access COVID-19 dataset. This work presents a 3-step technique to
fine-tune a pre-trained ResNet-50 architecture to improve model performance and
reduce training time. We call it COVIDResNet. This is achieved through
progressively re-sizing of input images to 128x128x3, 224x224x3, and 229x229x3
pixels and fine-tuning the network at each stage. This approach along with the
automatic learning rate selection enabled us to achieve the state of the art
accuracy of 96.23% (on all the classes) on the COVIDx dataset with only 41
epochs. This work presented a computationally efficient and highly accurate
model for multi-class classification of three different infection types from
along with Normal individuals. This model can help in the early screening of
COVID19 cases and help reduce the burden on healthcare systems.
