# CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image

arXiv: https://arxiv.org/abs/2005.03059 · HTML: https://mlsanity.com/paper/2005.03059/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 8 of 8)

- [FEDZIP: A Compression Framework for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2102.01593/) (2021)
- [Sounds of COVID-19: exploring realistic performance of audio-based digital testing](https://mlsanity.com/paper/2106.15523/) (2021)
- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification](https://mlsanity.com/paper/2211.14559/) (2022)
- [AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images](https://mlsanity.com/paper/2105.10239/) (2021)
- [DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19](https://mlsanity.com/paper/2108.01997/) (2021)
- [Weakly-supervised Generative Adversarial Networks for medical image classification](https://mlsanity.com/paper/2111.14605/) (2021)
- [Diagnosis of COVID-19 disease using CT scan images and pre-trained models](https://mlsanity.com/paper/2208.07829/) (2022)

## Abstract (arXiv)

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.