Publication: COVID-19 identification-based implementation on FPGA using CNN
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering::Electrical and electronic engineering | |
| dc.contributor.author | Ammar Hafiz bin Roslli | |
| dc.date.accessioned | 2025-05-15T08:49:18Z | |
| dc.date.available | 2025-05-15T08:49:18Z | |
| dc.date.issued | 2024-07 | |
| dc.description.abstract | Following the recovery from the Coronavirus (Covid-19) pandemic that occurred in 2019, the respiratory disease still remains as a lethal virus, despite improved treatments. Ongoing efforts to mitigate this harm includes research and development on efficient Covid-19 detection and diagnosis. Reliable methods such as chest radiological imaging and computed tomography techniques are still flawed by manual examinations, being prone to slow detection and human errors. This project presents an accelerated and accurate Covid-19 identifier via the implementation of the AlexNet Convolutional Neural Network (CNN) model on the DE1 SoC FPGA platform. The progression of this work comprises of 10 stages which involves CXR image dataset acquisition, dataset segmentation, Caffe framework installaton, LMDB formatting of dataset, dataset image mean generation, CNN model training, model weights extraction, confusion matrix evaluations, OpenCL environment setup, and PipeCNN compilation. Expected results for this project are quick operation times and favourable accuracy. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/21656 | |
| dc.language.iso | en | |
| dc.title | COVID-19 identification-based implementation on FPGA using CNN | |
| dc.type | Resource Types::text::report::technical report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |