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Tuberculosis
Chest X-ray, Classification, Deep Learning, Pulmonary, Convolutional Neural Network

Tuberculosis has claimed many lives, especially in developing countries. While treatment is possible, it requires an accurate diagnosis first to detect the presence of tuberculosis. Several screening techniques exist and the most reliable is the chest X-ray but the necessary radiological expertise for accurately interpreting the chest X-ray images is lacking. The task of manual examination of large chest X-ray images by the radiologist is time-consuming and could result in misdiagnosis as a result of a lack of expertise. Hence, a computer-aided diagnosis could perform this task quickly, accurately and drastically improve the ability to diagnose correctly and ultimately treat the disease earlier. As a result of the complexity that surrounds the manual diagnosis of chest X-ray, we propose a model that employs the use of learning algorithm (Convolutional Neural Network) to effectively learn the features associated with tuberculosis and make corresponding accurate predictions. Our model achieved 87.8% accuracy in classifying chest X-ray into abnormal and normal classes and validated against the ground-truth. Our model expresses a promising pathway in solving the diagnosis issue in early detection of tuberculosis manifestation and hope for the radiologist and medical healthcare facilities in the developing countries.