MIA-3DCNN: COVID-19 Detection Based on a 3D CNN
This work addresses the need for faster COVID-19 diagnosis to reduce clinic overload, but it is incremental as it applies a known method to a specific medical imaging task.
The paper tackles COVID-19 detection from CT images using a 3D CNN architecture, achieving promising results in a challenging dataset.
Early and accurate diagnosis of COVID-19 is essential to control the rapid spread of the pandemic and mitigate sequelae in the population. Current diagnostic methods, such as RT-PCR, are effective but require time to provide results and can quickly overwhelm clinics, requiring individual laboratory analysis. Automatic detection methods have the potential to significantly reduce diagnostic time. To this end, learning-based methods using lung imaging have been explored. Although they require specialized hardware, automatic evaluation methods can be performed simultaneously, making diagnosis faster. Convolutional neural networks have been widely used to detect pneumonia caused by COVID-19 in lung images. This work describes an architecture based on 3D convolutional neural networks for detecting COVID-19 in computed tomography images. Despite the challenging scenario present in the dataset, the results obtained with our architecture demonstrated to be quite promising.