Tuberculosis (TB), is an ancient disease that probably affects humans since pre-hominids. This disease is caused by bacteria belonging to the mycobacterium tuberculosis complex and usually affects the lungs in up to 67% of cases. In 2019, there were estimated to be over 10 million tuberculosis cases in the world, in the same year TB was between the ten leading causes of death, and the deadliest from a single infectious agent. Chest X-ray (CXR) has recently been promoted by the WHO as a tool possibly placed early in screening and triaging algorithms for TB detection. Numerous TB prevalence surveys have demonstrated that CXR is the most sensitive screening tool for pulmonary TB and that a significant proportion of people with TB are asymptomatic in the early stages of the disease. This study presents experimentation of classic convolutional neural network architectures on public CRX databases in order to create a tool applied to the diagnostic aid of TB in chest X-ray images. As result the study has an AUC ranging from 0.78 to 0.84, sensitivity from 0.76 to 0.86 and specificity from 0.58 to 0.74 depending on the network architecture. The observed performance by these metrics alone are within the range of metrics found in the literature, although there is much room for metrics improvement and bias avoiding. Also, the usage of the model in a triage use-case could be used to validate the efficiency of the model in the future.