- Published
- 01 September 2022
- Conference item
DECOVID-CT: Lightweight 3D CNN for COVID-19 Infection Prediction
- Authors
- Source
- 2022 IEEE International Conference on Consumer Electronics - Taiwan
Abstract
The COVID-19 pandemic has become a critical threat to global health and the economy since its first outbreak in 2019. The standard diagnosis for COVID-19, Reverse Transcription Polymerase Chain Reaction (RT-PCR) is time consuming, and has lower sensitivity compared to CT-scans. Therefore, CT-scans can be used as a complementary method, alongside RT-PCR tests for COVID-19 infection prediction. However, manually reviewing CT scans is time consuming. In this paper, we propose DECOVID-CT, a deep learning model based on 3D convolutional neural network (CNN) for the detection of COVID-19 infection with CT images. The model is trained and tested on the RICORD dataset, a multinational dataset, for higher robustness. Our model achieved an accuracy of 100%, for predicting COVID-19 positive images.
Rights
This content is not covered by the Open Government Licence. Please see source record or item for information on rights and permissions.
Cite as
Rithesh, K., Wong, L., See, J., Chan, W. & Ng, K. 2022, 'DECOVID-CT: Lightweight 3D CNN for COVID-19 Infection Prediction', 2022 IEEE International Conference on Consumer Electronics - Taiwan, pp. 363-364. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869238