  Early detection of COVID-19 is vital to control its spread. Deep learning
methods have been presented to detect suggestive signs of COVID-19 from chest
CT images. However, due to the novelty of the disease, annotated volumetric
data are scarce. Here we propose a method that does not require either large
annotated datasets or backpropagation to estimate the filters of a
convolutional neural network (CNN). For a few CT images, the user draws markers
at representative normal and abnormal regions. The method generates a feature
extractor composed of a sequence of convolutional layers, whose kernels are
specialized in enhancing regions similar to the marked ones, and the decision
layer of our CNN is a support vector machine. As we have no control over the CT
image acquisition, we also propose an intensity standardization approach. Our
method can achieve mean accuracy and kappa values of $0.97$ and $0.93$,
respectively, on a dataset with 117 CT images extracted from different sites,
surpassing its counterpart in all scenarios.
