# DCNNV-19: A Deep Convolutional Neural Network for COVID-19 Detection in Chest Computed Tomographies

arXiv: https://arxiv.org/abs/2208.09349 · HTML: https://mlsanity.com/paper/2208.09349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 18 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients](https://mlsanity.com/paper/2010.07468/) (2020)
- [Mish: A Self Regularized Non-Monotonic Activation Function](https://mlsanity.com/paper/1908.08681/) (2019)
- [COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning](https://mlsanity.com/paper/2101.07433/) (2021)

## Abstract (arXiv)

This technical report proposes the use of a deep convolutional neural network as a preliminary diagnostic method in the analysis of chest computed tomography images from patients with symptoms of Severe Acute Respiratory Syndrome (SARS) and suspected COVID-19 disease, especially on occasions when the delay of the RT-PCR result and the absence of urgent care could result in serious temporary, long-term, or permanent health damage. The model was trained on 83,391 images, validated on 15,297, and tested on 22,185 figures, achieving an F1-Score of 98%, 97.59% in Cohen's Kappa, 98.4% in Accuracy, and 5.09% in Loss. Attesting a highly accurate automated classification and providing results in less time than the current gold-standard exam, Real-Time reverse-transcriptase Polymerase Chain Reaction (RT-PCR).