ComputeCOVID19+: Accelerating COVID-19 Diagnosis and Monitoring via High-Performance Deep Learning on CT Images
| dc.contributor.author | Goel, Garvit | en |
| dc.contributor.author | Gondhalekar, Atharva | en |
| dc.contributor.author | Qi, Jingyuan | en |
| dc.contributor.author | Zhang, Zhicheng | en |
| dc.contributor.author | Cao, Guohua | en |
| dc.contributor.author | Feng, Wu-chun | en |
| dc.date.accessioned | 2024-03-04T15:12:15Z | en |
| dc.date.available | 2024-03-04T15:12:15Z | en |
| dc.date.issued | 2021-10-05 | en |
| dc.description.abstract | The 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%. | en |
| dc.description.version | Accepted version | en |
| dc.format.extent | 11 page(s) | en |
| dc.identifier.doi | https://doi.org/10.1145/3472456.3473523 | en |
| dc.identifier.isbn | 9781450390682 | en |
| dc.identifier.issn | 0190-3918 | en |
| dc.identifier.orcid | Feng, Wu-chun [0000-0002-6015-0727] | en |
| dc.identifier.orcid | Cao, Guohua [0000-0003-2107-7587] | en |
| dc.identifier.uri | https://hdl.handle.net/10919/118250 | en |
| dc.language | English | en |
| dc.publisher | ACM | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | AI | en |
| dc.subject | biomedical imaging | en |
| dc.subject | COVID-19 | en |
| dc.subject | computed tomography | en |
| dc.subject | coronavirus | en |
| dc.subject | deep learning | en |
| dc.subject | FPGA | en |
| dc.subject | GPU | en |
| dc.subject | neural network | en |
| dc.title | ComputeCOVID19+: Accelerating COVID-19 Diagnosis and Monitoring via High-Performance Deep Learning on CT Images | en |
| dc.title.serial | 50TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING | en |
| dc.type | Conference proceeding | en |
| dc.type.other | Proceedings Paper | en |
| dc.type.other | Book in series | en |
| pubs.finish-date | 2021-08-12 | en |
| pubs.organisational-group | /Virginia Tech | en |
| pubs.organisational-group | /Virginia Tech/Engineering | en |
| pubs.organisational-group | /Virginia Tech/Engineering/Computer Science | en |
| pubs.organisational-group | /Virginia Tech/Faculty of Health Sciences | en |
| pubs.organisational-group | /Virginia Tech/All T&R Faculty | en |
| pubs.organisational-group | /Virginia Tech/Engineering/COE T&R Faculty | en |
| pubs.start-date | 2021-08-09 | en |