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Early and accurate diagnosis of COVID-19 is essential to control the rapidspread of the pandemic and mitigate sequelae in the population. Currentdiagnostic methods, such as RT-PCR, are effective but require time to provideresults and can quickly overwhelm clinics, requiring individual laboratoryanalysis. Automatic detection methods have the potential to significantlyreduce diagnostic time. To this end, learning-based methods using lung imaginghave been explored. Although they require specialized hardware, automaticevaluation methods can be performed simultaneously, making diagnosis faster.Convolutional neural networks have been widely used to detect pneumonia causedby COVID-19 in lung images. This work describes an architecture based on 3Dconvolutional neural networks for detecting COVID-19 in computed tomographyimages. Despite the challenging scenario present in the dataset, the resultsobtained with our architecture demonstrated to be quite promising.

MIA-3DCNN: COVID-19 Detection Based on a 3D CNN