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The great challenge for the humanity of the year 2020 is the fight againstCOVID-19. The whole world is making a huge effort to find an effective vaccinewith purpose to protect people not yet infected. The alternative solutionremains early diagnosis, carried out through real-time polymerase chainreaction (RT-PCR) test or thorax computer tomography (CT) scan images. Deeplearning algorithms, specifically convolutional neural networks, represent amethodology for the image analysis. They optimize the classification designtask, essential for an automatic approach on different types of images,including medical. In this paper, we adopt pretrained deep convolutional neuralnetwork architectures in order to diagnose COVID-19 disease on CT images. Ouridea is inspired by what the whole of humanity is achieving, substantially theset of multiple contributions is better than the single one for the fightagainst the pandemic. Firstly, we adapt, and subsequently retrain, for ourassumption some neural architectures adopted in other application domains.Secondly, we combine the knowledge extracted from images by neuralarchitectures in an ensemble classification context. Experimental phase isperformed on CT images dataset and results obtained show the effectiveness ofthe proposed approach with respect to state-of-the-art competitors.

Fighting together against the pandemic: learning multiple models on tomography images for COVID-19 diagnosis