TY - CPAPER
AU - Rithesh, Kannan
AU - Wong, Lai-Kuan
AU - See, John
AU - Chan, Wai-Yee
AU - Ng, Kwan-Hong
PY - 2022
DA - September
TI - DECOVID-CT: Lightweight 3D CNN for COVID-19 Infection Prediction
EP - 364
DO - https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869238
AB - 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.
PB - IEEE
UR - https://researchportal.hw.ac.uk/en/publications/c7442d5f-4586-40f8-9b90-b39a3efd9b6b
KW - Coronavirus (COVID-19)
KW - Digital health and technology
ER