Federated learning in the detection of Covid -19 in patient Ct-Scans: A practical evaluation of external generalisation
| dc.contributor.author | Wapenaar, Korstiaan | |
| dc.contributor.supervisor | Ranchod, Pravesh | |
| dc.date.accessioned | 2024-10-22T15:01:35Z | |
| dc.date.available | 2024-10-22T15:01:35Z | |
| dc.date.issued | 2023-08 | |
| dc.description | A research report submitted in partial fulfilment of the requirements for the degree of Master of Science, to the Faculty of Science, School of Computer Science & Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023. | |
| dc.description.abstract | This research explores the practical utility of using convolutional neural networks in a federated learning architecture for COVID-19 diagnostics using chest CT-scans, and whether federated learning models can generalise to data from healthcare facilities that did not participate in training. A model that can generalise to these healthcare facilities could provide lower-resourced or over-utilised facilities with access to supplementary diagnostic services. Eleven models are trained using a modified VGG-16. The models are trained using data from five ‘sites’: four sites are single healthcare facilities and the fifth site is a composite of data from a variety of healthcare facilities. Eleven models are trained, evaluated and compared: five ‘independent models’ are each trained with data from a single site; three ‘global models’ are trained using centrally pooled data from a variety of sites; three ‘federated models’ are trained using a federated averaging approach. The site with composite data is held-out and never included in training the federated and global models. With the exception of this composite site, all models achieve a test accuracy of at least 0.93 when evaluated using test data from the sites used in training these models. All models are then evaluated using data from the composite site. The global and federated models achieve a 0.5 to 0.6 accuracy for the composite site, indicating that the model and training regime is unable to achieve useful accuracies for sites non-participant in training. The federated models are therefore not accurate enough to motivate a healthcare facility decision maker to use the federated models as an alternative or supplementary diagnostic tool to radiographers, or to developing their own independent model. Evaluation of the results suggests that high-quality and consistent image pre-processing may be a necessary precondition for the task. | |
| dc.description.submitter | MM2024 | |
| dc.faculty | Faculty of Science | |
| dc.identifier | 0000-0003-2248-7019 | |
| dc.identifier.citation | Wapenaar, Korstiaan. (2023). Federated learning in the detection of Covid -19 in patient Ct-Scans: A practical evaluation of external generalisation. [Master's dissertation, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/41823 | |
| dc.identifier.uri | https://hdl.handle.net/10539/41823 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Computer Science and Applied Mathematics | |
| dc.subject | Federated Learning | |
| dc.subject | COVID-19 | |
| dc.subject | Convolutional neural networks | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-3: Good health and well-being | |
| dc.title | Federated learning in the detection of Covid -19 in patient Ct-Scans: A practical evaluation of external generalisation | |
| dc.type | Dissertation |