Tuberculosis (TB) is curable, and millions of deaths could be averted if diagnosed early. One of the sources of screening for TB is chest x-rays. Still, its success depends on the interpretation of skilled and experienced radiologists, mostly lacking in high TB burden regions. However, with the intervention of a computer-aided detection system, TB can be automatically detected from chest x-rays. This paper presents an Ensemble model based on multiple pre-trained models to automatically detect TB from chest x-rays. The models were trained on the Shenzhen dataset and validated on the Montgomery dataset to achieve good generalization on a new (unseen) dataset. The proposed Ensemble model achieved high accuracy and sensitivity that is comparable with state-of-the-art models and outperformed existing Ensemble models aimed at Tuberculosis classification.