Here is presented the material that we used to implement different convolutional neural networks to classify 2D CT images
of COVID-19 and non-COVID-19 infected people.

The '.py' files are the codes for all the different architectures that we tried, although ResNet50-V1 and ResNet50-V2 do
not seem to work.

The training was done with the data-set presented at the CT diagnosis COVID-19 challenge
(https://covid-ct.grand-challenge.org/).

More information about how we created the algorithms and the results is presented at the .pdf file, which is the main
article of our project.

The compressed folder contains the source code for a user interface in which the clinician, besides classifying the image
under the weights of the simple CNN, is able to do some preprocessing on the image and add clinical information.