@proceedings{Rithesh_K-2022_98230,
title = {DECOVID-CT: Lightweight 3D CNN for COVID-19 Infection Prediction},
author = {Rithesh, K. and Wong, L. and See, J. and Chan, W. and Ng, K.},
month = {sep},
year = {2022},
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.},
pages = {363-364},
journal = {2022 IEEE International Conference on Consumer Electronics - Taiwan},
publisher = {IEEE},
url = {https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869238},
}
