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.


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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

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Last updated: 11 October 2022
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