| dc.contributor.author | Mirembe, Betty | |
| dc.date.accessioned | 2022-05-03T11:40:16Z | |
| dc.date.available | 2022-05-03T11:40:16Z | |
| dc.date.issued | 2022-01-22 | |
| dc.identifier.citation | Mirembe, Betty. (2022). Machine learning-aided screening of COVID-19 in Lung Ultrasound images. (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda. | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/12061 | |
| dc.description | A final year project report submitted in partial fulfillment of the requirements for the award of the degree of Bachelor of Science in
Telecommunications Engineering of Makerere University. | en_US |
| dc.description.abstract | In this project, the transfer learning technique was exploited on several deep learning algorithms to classify COVID-19 in Lung Ultrasound images. Training and preliminary testing of the algorithms was performed with the use of a data set of 792 images that contained several distinct features that are indicative of either of the classes of interest i.e. COVID-19 and healthy. Particularly the VGG-16 framework provided outstanding optimal results with a remarkable accuracy of 97.5 percent and a recall of 95.7 percent. The astounding success rate makes the model a very useful advisory screening approach in this field of medical imaging to aid further diagnosis of COVID-19. | en_US |
| dc.description.sponsorship | Makerere University, Research and Innovation Fund | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Lung Ultrasound images. | en_US |
| dc.title | Machine learning-aided screening of COVID-19 in Lung Ultrasound images. | en_US |
| dc.type | Thesis | en_US |
