# Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imaging

arXiv: https://arxiv.org/abs/2104.01509 · HTML: https://mlsanity.com/paper/2104.01509/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 4 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)](https://mlsanity.com/paper/2004.12084/) (2020)

## Built on by (most cited first, 1 of 1)

- [Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies](https://mlsanity.com/paper/2411.05029/) (2024)

## Abstract (arXiv)

The new coronavirus 2019 (COVID-2019) has rapidly become a pandemic and has had a devastating effect on both everyday life, public health and the global economy. It is critical to detect positive cases as early as possible to prevent the further spread of this epidemic and to treat affected patients quickly. The need for auxiliary diagnostic tools has increased as accurate automated tool kits are not available. This paper presents a work in progress that proposes the analysis of images of lung ultrasound scans using a convolutional neural network. The trained model will be used on a Raspberry Pi to predict on new images.