DECOVID-CT: Lightweight 3D CNN for COVID-19 Infection Prediction


Rithesh, Kannan and Wong, Lai Kuan and See, John and Chan, Wai Yee and Ng, Kwan Hong (2022) DECOVID-CT: Lightweight 3D CNN for COVID-19 Infection Prediction. In: 2022 IEEE International Conference on Consumer Electronics - Taiwan, 6 - 8 July 2022, Taipei, Taiwan.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: COVID-19, Solid modeling, Three-dimensional displays, Sensitivity, Computed tomography, Computational modeling, Predictive models
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 31 Oct 2022 07:35
Last Modified: 31 Oct 2022 07:35


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