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

arXiv: https://arxiv.org/abs/2303.10738 · HTML: https://mlsanity.com/paper/2303.10738/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging](https://mlsanity.com/paper/2206.04732/) (2022)
- [Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans](https://mlsanity.com/paper/2207.01579/) (2022)
- [FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis](https://mlsanity.com/paper/2207.01758/) (2022)
- [Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets](https://mlsanity.com/paper/2207.12218/) (2022)

## Abstract (arXiv)

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.