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dc.contributor.authorLubwama, Emmanuel
dc.date.accessioned2023-01-16T08:31:10Z
dc.date.available2023-01-16T08:31:10Z
dc.date.issued2023
dc.identifier.citationLubwama, Emmanuel. (2022). Machine learning aided screening of lung diseases in CT images. (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12281/14238
dc.descriptionA final year project report submitted in partial fulfillment of the requirements for the award of the degree of Bachelor of Science in Computer Engineeringen_US
dc.description.abstractChest computed tomography (CT) scan image screening for lung diseases is a laborious and time-consuming technique that can only be handled by qualified radiologists. Due to a shortage of experienced radiologists in Uganda, the workload has increased, which could eventually result in fatal diagnostic errors brought on by exhaustion. Radiologists can assist patients by using machine learning-aided lung disease screening in chest CT scan images to balance the workload and lessen the likelihood of these errors. In this study, we propose an automated decision support system with a convolutional neural network model built on the ResNet50 architecture that receives a chest CT scan image as an input and returns the probability distribution of the possible presence of any of the four categories; covid-19, healthy, lung cancer and pneumonia in the chest CT scan image. A total of 379 chest CT scan images were included in the dataset used to test the model, and they were divided into four categories; covid-19, healthy, lung cancer and pneumonia. The model achieved an accuracy of 96of 96.25en_US
dc.language.isoenen_US
dc.publisherMakerere Universityen_US
dc.subjectMachine learningen_US
dc.subjectAided screeningen_US
dc.subjectLung diseasesen_US
dc.subjectCT imagesen_US
dc.titleMachine learning aided screening of lung diseases in CT images.en_US
dc.typeThesisen_US


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