Document Type : Other

Authors

• Simon Kawuma ¹
• Elias Kumbakumba ²
• Vicent Mabirizi ³
• Deborah Nanjebe ⁴
• Kenneth Mworozi ⁵
• Adolf Oyesigye Mukama ⁶
• Lydia Kyasimire ²

¹ Software Engineering, Mbarara University of Science and Technology, Mbarara, Uganda.

² Department of Paediatrics, Mbarara University of Science and Technology, Mbarara, Uganda.

³ Department of Information Technology, Kabale University, Kabale, Uganda

⁴ Epicentre Mbarara Research Centre, Mbarara, Uganda.

⁵ Department of radiology, Mbarara University of Science and Technology, Mbarara, Uganda.

⁶ Medical Laboratory Science, Epicentre Mbarara Research Centre, Mbarara, Uganda.

Abstract

Tuberculosis (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 difficulty and its high mortality rate is due to many gaps in the diagnosis of this illness especially among children. As a strategy to curb TB mortality rate in children, there exist a need to improve and expedite the screening for TB among children. Chest X-ray (CXR) are commonly used in TB burden countries like Uganda to diagnose TB patients but interpretation of the patients’ 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 transfer learning 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. Transfer learning 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 analyzing CXR images.

Keywords

• Artificial Intelligence
• models
• Deep learning

Main Subjects

• H.6.2. Models