Goel, GarvitGondhalekar, AtharvaQi, JingyuanZhang, ZhichengCao, GuohuaFeng, Wu-chun2024-03-042024-03-042021-10-0597814503906820190-3918https://hdl.handle.net/10919/118250The COVID-19 pandemic has highlighted the importance of diagnosis and monitoring as early and accurately as possible. However, the reverse-transcription polymerase chain reaction (RT-PCR) test results in two issues: (1) protracted turnaround time from sample collection to testing result and (2) compromised test accuracy, as low as 67%, due to when and how the samples are collected, packaged, and delivered to the lab to conduct the RT-PCR test. Thus, we present ComputeCOVID19+, our computed tomography-based framework to improve the testing speed and accuracy of COVID-19 (plus its variants) via a deep learning-based network for CT image enhancement called DDnet, short for DenseNet and Deconvolution network. To demonstrate its speed and accuracy, we evaluate ComputeCOVID19+ across several sources of computed tomography (CT) images and on many heterogeneous platforms, including multi-core CPU, many-core GPU, and even FPGA. Our results show that ComputeCOVID19+ can significantly shorten the turnaround time from days to minutes and improve the testing accuracy to 91%.11 page(s)In CopyrightAIbiomedical imagingCOVID-19computed tomographycoronavirusdeep learningFPGAGPUneural networkConference proceedinghttps://doi.org/10.1145/3472456.3473523Feng, Wu-chun [0000-0002-6015-0727]Cao, Guohua [0000-0003-2107-7587]