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dc.contributor.authorKawuma, Simon
dc.contributor.authorKumbakumba, Elias
dc.contributor.authorVicent, Mabirizi
dc.contributor.authorNanjebe, Deborah
dc.contributor.authorMworozi, Kenneth
dc.contributor.authorMukama, Adolf Oyesigye
dc.contributor.authorKyasimire, Lydia
dc.date.accessioned2024-10-02T13:41:21Z
dc.date.available2024-10-02T13:41:21Z
dc.date.issued2024
dc.identifier.citationKawuma, S., Kumbakumba, E., Mabirizi, V., Nanjebe, D., Mworozi, K., Oyesigye Mukama, A., & Kyasimire, L. (2024). Diagnosis and Classification of Tuberculosis Chest X-ray Images of Children Less Than 15 years at Mbarara Regional Referral Hospital Using Deep Learning. Journal of AI and Data Mining.en_US
dc.identifier.urihttp://ir.must.ac.ug/xmlui/handle/123456789/3849
dc.description.abstractTuberculosis (TB) is an underestimated cause of death in children, with only 45% of cases correctly diagnosed and reported. It is estimated that 1.12 million TB cases occurred among newborns, children, and adolescents aged less or equal 14 years. In Uganda, TB prevalence is 8.5% in children and 16.7% in adolescents. Treatment and diagnosing TB is challenging and its high mortality rate is due to many lacks in the diagnosis of this illness especially among children. As a strategy to curb TB mortality rate in children, there exists a need to improve and expedite the screening for TB among children. Chest X-ray (CXR) is commonly used in TB burdened countries like Uganda to diagnose TB patients but interpretation of the patient’s radiograph needs skilled radiologists who are few. To this end, this research aims to close the TB mortality gap in children by applying AI, primarily deep learning techniques, to detect TB in children. The study created five models, one from scratch and four pre-trained Transfer Learning (TL) and were trained and verified using digital CXR radiograph images of children who visit the TB clinic at Mbarara Regional Referral Hospital. The model classifies clinical images of patients into normal or Tuberculosis. TL models; VGG16, VGG19, Inception V3, and ResNet50 outperformed scratch model with validation accuracy of 79.91%, 69.21%, 53.0%, 51.09% and 50.01% respectively. We hope that once the deep learning models are implemented and adopted by the radiologist, it will reduce the time spent by radiologist while analysing CXR images.en_US
dc.language.isoen_USen_US
dc.publisherJournal of AI and Data Miningen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectModelsen_US
dc.subjectDeep learningen_US
dc.subjectTuberculosisen_US
dc.subjectChest X-rayen_US
dc.subjectConvolution neural networken_US
dc.titleDiagnosis and Classification of Tuberculosis Chest X-ray Images of Children Less Than 15 years at Mbarara Regional Referral Hospital Using Deep Learningen_US
dc.typeArticleen_US


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