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Early detection of COVID-19 is vital to control its spread. Deep learningmethods have been presented to detect suggestive signs of COVID-19 from chestCT images. However, due to the novelty of the disease, annotated volumetricdata are scarce. Here we propose a method that does not require either largeannotated datasets or backpropagation to estimate the filters of aconvolutional neural network (CNN). For a few CT images, the user draws markersat representative normal and abnormal regions. The method generates a featureextractor composed of a sequence of convolutional layers, whose kernels arespecialized in enhancing regions similar to the marked ones, and the decisionlayer of our CNN is a support vector machine. As we have no control over the CTimage acquisition, we also propose an intensity standardization approach. Ourmethod 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.

CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images