dc.contributor.author | Owino, Astone, O | |
dc.date.accessioned | 2021-12-01T08:55:16Z | |
dc.date.available | 2021-12-01T08:55:16Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | http://erepository.uonbi.ac.ke/handle/11295/155812 | |
dc.description.abstract | COVID-19 is a worldwide pandemic since the beginning of 2020. It records a high death rate
across many countries in the world. Efforts have been put in place to control the spread and the
associated deaths. Vaccination, isolation, mass testing, and artificial intelligence models have
been used to control the disease. Due to the high number of cases per day, manual monitoring of
progression has been difficult and associated with false negatives. Ground-Glass opacities
(GGOs) identification has been used in the detection and classification of COVID-19 positive
and negative cases. The localization of the GGOs and the volumes in the lungs can be used to
identify and monitor the COVID-19 progression in the lungs. This research developed an
adoptive computational model to help in COVID-19 monitoring. It identifies the localization of
GGOs in the lungs responsible for COVID-19. The research also used automated feature
extraction convolutional neural networks (CNN) models to enhance speed and accuracy. Feature
extraction and modeling were done with standard CNN and CNN with transfer learning with
augmentation models. CNN with the transfer learning model was chosen for the implementation
because of the high accuracy of 97.36%. The model was used to identify GGOs given new
examples to classify COVID-19 positive and negative cases accurately. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Nairobi | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Ground-glass opacities, COVID-19, convolutional neural networks, progression | en_US |
dc.title | Ground-glass opacities identification using neural networks for monitoring Covid-19 progression | en_US |
dc.type | Thesis | en_US |