Abstract:
The mycobacterium tuberculosis causes the chronic necrotizing disease known as
tuberculosis. Complex pulmonary tuberculosis is a bacterial infection brought on by the
easily airborne Mycobacterium TB. The primary infection is caused by mycobacterium
TB, which gradually multiplies in the lungs after becoming infected. Frequent medical
types of active lung TB symptoms and indicators include fever, coughing, weight loss,
hemoptysis, and night sweats. The lack of human resources and radiological
interpretation know-how weakens TB screening programs, especially in TB endemic
countries. Additionally, poor judgment, a lack of proficiency, and diagnostic mistakes
can directly result in patient damage or death. Making the right choice and automatically
detecting CXR can serve as a reliable substitute for more complex and technically
demanding methods. So that to address this problem, we created an AI model that
automatically detect the existence of Pulmonary Tuberculosis disease on human beings.
The model attempts to support physicians and patients for the diagnosis and treatment of
the disease. We collected the necessary dataset from Jimma University medical center.
We used image processing techniques that include image preprocessing, segmentation,
feature extraction, and detection. We compared the newly proposed TBDMobileNetV2
model with other state-of-the-art models. TBDMobileNetV2 achieved 96.11% testing
accuracy; AlexNet model achieved 83.39% testing accuracy; and VGGNet model
achieved 73.33% testing accuracy. So that the proposed TBDMobileNetV2 model
outperforms the other models f or the detection of Pulmonary Tuberculosis disease.
