# POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)

arXiv: https://arxiv.org/abs/2004.12084 · HTML: https://mlsanity.com/paper/2004.12084/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2020.

## Built on by (most cited first, 26 of 26)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge](https://mlsanity.com/paper/2101.07511/) (2021)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics](https://mlsanity.com/paper/2103.10003/) (2021)
- [Large-scale Long-tailed Disease Diagnosis on Radiology Images](https://mlsanity.com/paper/2312.16151/) (2023)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining](https://mlsanity.com/paper/2207.13148/) (2022)
- [POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasound](https://mlsanity.com/paper/2105.09913/) (2021)
- [FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features](https://mlsanity.com/paper/2303.07852/) (2023)
- [Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection](https://mlsanity.com/paper/2201.10166/) (2022)
- [Ultrasound Lung Aeration Map via Physics-Aware Neural Operators](https://mlsanity.com/paper/2501.01157/) (2025)
- [Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review](https://mlsanity.com/paper/2110.14910/) (2021)
- [Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound](https://mlsanity.com/paper/2201.07357/) (2022)
- [COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging](https://mlsanity.com/paper/2108.03131/) (2021)
- [Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks](https://mlsanity.com/paper/2309.02596/) (2023)
- [Adaptive Few-Shot Learning PoC Ultrasound COVID-19 Diagnostic System](https://mlsanity.com/paper/2109.03793/) (2021)
- [The Role of Pleura and Adipose in Lung Ultrasound AI](https://mlsanity.com/paper/2201.07368/) (2022)
- [A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images](https://mlsanity.com/paper/2309.02555/) (2023)
- [Intra-video Positive Pairs in Self-Supervised Learning for Ultrasound](https://mlsanity.com/paper/2403.07715/) (2024)
- [VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos](https://mlsanity.com/paper/2507.21863/) (2025)
- [Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imaging](https://mlsanity.com/paper/2104.01509/) (2021)
- [Implementing a Detection System for COVID-19 based on Lung Ultrasound Imaging and Deep Learning](https://mlsanity.com/paper/2106.10651/) (2021)
- [Lung Ultrasound Segmentation and Adaptation between COVID-19 and Community-Acquired Pneumonia](https://mlsanity.com/paper/2108.03138/) (2021)
- [Auto-US: An Ultrasound Video Diagnosis Agent Using Video Classification Framework and LLMs](https://mlsanity.com/paper/2511.07748/) (2025)
- [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)
- [A Controlled Audit of Architectural Complexity in Uncertainty-Aware Multi-Organ Ultrasound Classification](https://mlsanity.com/paper/2608.28063/) (2026)

## Abstract (arXiv)

With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic systems were demonstrated promising performances using CT or X-ray data. Here, we advocate a more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection. Ultrasound is non-invasive and ubiquitous in medical facilities around the globe. Our contribution is threefold. First, we gather a lung ultrasound (POCUS) dataset consisting of 1103 images (654 COVID-19, 277 bacterial pneumonia and 172 healthy controls), sampled from 64 videos. This dataset was assembled from various online sources, processed specifically for deep learning models and is intended to serve as a starting point for an open-access initiative. Second, we train a deep convolutional neural network (POCOVID-Net) on this 3-class dataset and achieve an accuracy of 89% and, by a majority vote, a video accuracy of 92% . For detecting COVID-19 in particular, the model performs with a sensitivity of 0.96, a specificity of 0.79 and F1-score of 0.92 in a 5-fold cross validation. Third, we provide an open-access web service (POCOVIDScreen) that is available at: https://pocovidscreen.org. The website deploys the predictive model, allowing to perform predictions on ultrasound lung images. In addition, it grants medical staff the option to (bulk) upload their own screenings in order to contribute to the growing public database of pathological lung ultrasound images. Dataset and code are available from: https://github.com/jannisborn/covid19_pocus_ultrasound. NOTE: This preprint is superseded by our paper in Applied Sciences: https://doi.org/10.3390/app11020672